Fabrication of amorphous metal oxide/p-BiVO4 photocathode: understanding the role of entropy for reducing nitrate to ammonia

The controllable design of chemical microenvironment with the expected thermodynamics and kinetics for boosting catalytic activity and selectivity is still challenging. Herein, an amorphous metal oxide (A-MxOy) was employed to...

What do cellular responses to acidity tell us about cancer?

The notion that invasive cancer is a product of somatic evolution is a well-established theory that can be modelled mathematically and demonstrated empirically from therapeutic responses. Somatic evolution is by no means deterministic, and ample opportunities exist to steer its trajectory towards cancer cell extinction. One such strategy is to alter the chemical microenvironment shared between host and cancer cells in a way that no longer favours the latter. Ever since the first description of the Warburg effect, acidosis has been recognised as a key chemical signature of the tumour microenvironment. Recent findings have suggested that responses to acidosis, arising through a process of selection and adaptation, give cancer cells a competitive advantage over the host. A surge of research efforts has attempted to understand the basis of this advantage and seek ways of exploiting it therapeutically. Here, we review key findings and place these in the context of a mathematical framework. Looking ahead, we highlight areas relating to cellular adaptation, selection, and heterogeneity that merit more research efforts in order to close in on the goal of exploiting tumour acidity in future therapies.

Kinetic Interval Measurement: A Tool to Characterize Thermal Reversion Dynamics of Light-switchable Fluorescent Proteins

Light-switchable proteins like Light-Oxygen-Voltage (LOV) domains can be used to control protein interactions and have been applied in vivo to manipulate cell behavior. The switching between dark and light state of LOV domains depends on temperature or their chemical microenvironment and can be tuned by point mutations. Here, we present a method called Kinetic Interval Measurement (KIM) to quantify the thermal reversion dynamics of light-switchable proteins by using a custom microplate reader. We show that this versatile method can be used to determine the reversion half-life of the excited state of LOV proteins in a reproducible, fast and simple manner consuming only small amounts of protein. The sensitivity of the method allows to report on changes in temperature and imidazole concentration as well as the photoswitching dynamics of LOV proteins in living cells.

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

Flow and epiphyte growth effects on the thermal, optical and chemical microenvironment in the leaf phyllosphere of seagrass ( Zostera marina )

Intensified coastal eutrophication can result in an overgrowth of seagrass leaves by epiphytes, which is a major threat to seagrass habitats worldwide, but little is known about how epiphytic biofilms affect the seagrass phyllosphere. The physico-chemical microenvironment of Zostera marina L. leaves with and without epiphytes was mapped with electrochemical, thermocouple and scalar irradiance microsensors as a function of four irradiance conditions (dark, low, saturating and high light) and two water flow velocities (approx. 0.5 and 5 cm s −1 ), which resemble field conditions. The presence of epiphytes led to the build up of a diffusive boundary layer and a thermal boundary layer which impeded O 2 and heat transfer between the leaf surface and the surrounding water, resulting in a maximum increase of 0.8°C relative to leaves with no epiphytes. Epiphytes also reduced the quantity and quality of light reaching the leaf, decreasing plant photosynthesis. In darkness, epiphyte respiration exacerbated hypoxic conditions, which can lead to anoxia and the production of potential phytotoxic nitric oxide in the seagrass phyllosphere. Epiphytic biofilm affects the local phyllosphere physico-chemistry both because of its metabolic activity (i.e. photosynthesis/respiration) and its physical properties (i.e. thickness, roughness, density and back-scattering properties). Leaf tissue warming can lead to thermal stress in seagrasses living close to their thermal stress threshold, and thus potentially aggravate negative effects of global warming.

FLIMJ: an open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state and is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin, and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing against industry FLIM analysis standards.

The use of surfactant-filled mesoporous silica as an immobilising medium for a fluorescence lifetime pH indicator, providing long-term calibration stability

A material support is proposed which grants long-term stability for fluorescence lifetime pH measurements due to a homogeneous chemical microenvironment.

A Chemical Approach to Optimizing Bioactive Glass Dental Composites

The chemical microenvironment surrounding dental composites plays a crucial role in controlling the bacteria grown on these specialized surfaces. In this study, we report a scanning electrochemical microscopy (SECM)–based analytic technique to design and optimize metal ion-releasing bioactive glass (BAG) composites, which showed a significant reduction in biofilm growth. SECM allows positioning of the probe without touching the substrate while mapping the chemical parameters in 3-dimensional space above the substrate. Using SECM and a solid-state H+ and Ca2+ ion-selective microprobe, we determined that the local Ca2+ concentration released by different composites was 10 to 224 µM for a BAG particle size of <5 to 150 µm in the presence of artificial saliva at pH 4.5. The local pH was constant above the composites in the same saliva solution. The released amount of Ca2+ was determined to be maximal for particles <38 µm and a BAG volume fraction of 0.32. This optimized BAG-resin composite also showed significant inhibition of biofilm growth (24 ± 5 µm) in comparison with resin-only composites (53 ± 6 µm) after Streptococcus mutans bacteria were grown for 3 d in a basal medium mucin solution. Biofilm morphology and its subsequent volume, as determined by the SECM imaging technique, was (0.59 ± 0.38) × 107 µm3 for BAG-resin composites and (1.29 ± 0.53) × 107 µm3 for resin-only composites. This study thus lays the foundation for a new analytic technique for designing dental composites that are based on the chemical microenvironment created by biomaterials to which bacteria have been exposed.