Filamentous fungi play a key role as decomposers in Earth’s nutrient cycles. In soils, substrates are heterogeneously distributed in microenvironments. Hence, individual hyphae of a mycelium may experience very different environmental conditions simultaneously. In the current work, we investigated how fungi cope with local environmental variations at single-cell level. We developed a method based on infrared spectroscopy that allows the direct, in-situ chemical imaging of the decomposition activity of individual hyphal tips. Colonies of the ectomycorrhizal Basidiomycete Paxillus involutus were grown on liquid media, while parts of colonies were allowed to colonize lignin patches. Oxidative decomposition of lignin by individual hyphae growing under different conditions was followed for a period of seven days. We identified two sub-populations of hyphal tips: one with low decomposition activity and one with much higher activity. Active cells secreted more extracellular polymeric substances and oxidized lignin more strongly. The ratio of active to inactive hyphae strongly depended on the environmental conditions in lignin patches, but was further mediated by the decomposition activity of entire mycelia. Phenotypic heterogeneity occurring between genetically identical hyphal tips may be an important strategy for filamentous fungi to cope with heterogeneous and constantly changing soil environments.

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

We present a theoretical model of laser heating carbon nanotubes to determine the temperature profile during laser irradiation. Laser heating carbon nanotubes is an essential physics phenomenon in many aspects such as materials science, pharmacy, and medicine. In the present article, we explain the applications of carbon nanotubes for photoacoustic imaging contrast agents and photothermal therapy heating agents by evaluating the heat propagation in the carbon nanotube and its surrounding. Our model is constructed by applying the classical heat conduction equation. To simplify the problem, we assume the carbon nanotube is a solid cylinder with the length of the tube much larger than its diameter. The laser spot is also much larger than the dimension of carbon nanotubes. Consequently, we can neglect the length of tube dependence. Theoretically, we show that the temperature during laser heating is proportional to the diameter of carbon nanotube. Based on the solution of our model, we suggest using the larger diameter of carbon nanotubes to maximize the laser heating process. These results extend our understanding of the laser heating carbon nanotubes and provide the foundation for future technologically applying laser heating carbon nanotubes.

Photoacoustic (PA) imaging has the ability to visualize human organs with high spatial resolution and high contrast. Like digital images, PA images are contaminated with random noise due to some parameters. The band-pass filter does not effectively remove the noise because noise is randomly distributed in the bandwidth frequency. We present noise removal method in PA images by using non local means denoising (NLMD) method. The NLMD can be used if there are similarities or redundancies in the image. PA images contain of blood vessel which repeating on the small patch. The method was tested on PA images of carbon nanotubes in micropipe, in vivo mice brain and in vivo mice ear. We estimated the suggested input parameters of NLMD, so it can be automatically applied after scanning the image in PA imaging system. Our results declared that the NLMD enhanced the image quality of PA images.