Returning Individual Tap Water Testing Results to Research Study Participants after a Wildfire Disaster

After the devastating wildfire that destroyed most of the town of Paradise, California in 2018, volatile organic compounds were found in water distribution pipes. Approximately 11 months after the fire, we collected tap water samples from 136 homes that were still standing and tested for over 100 chemicals. Each participant received a customized report showing the laboratory findings from their sample. Our goal was to communicate individual water results and chemical information rapidly in a way that was understandable, scientifically accurate, and useful to participants. On the basis of this process, we developed a framework to illustrate considerations and priorities that draw from best practices of previous environmental results return research and crisis communication, while also addressing challenges specific to the disaster context. We also conducted a follow-up survey on participants’ perceptions of the results return process. In general, participants found the results return communications to be understandable, and they felt less worried about their drinking water quality after receiving the information. Over one-third of the participants reported taking some kind of action around their water usage habits after receiving their results. Communication with participants is a critical element of environmental disaster research, and it is important to have a strategy to communicate results that achieves the goals of timeliness, clarity, and scientific accuracy, ultimately empowering people toward actions that can reduce exposure.

Integrated near-infrared spectral sensing

Spectral sensing is increasingly used in applications ranging from industrial process monitoring to agriculture. Sensing is usually performed by measuring reflected or transmitted light with a spectrometer and processing the resulting spectra. However, realizing compact and mass-manufacturable spectrometers is a major challenge, particularly in the infrared spectral region where chemical information is most prominent. Here we propose a different approach to spectral sensing which dramatically simplifies the requirements on the hardware and allows the monolithic integration of the sensors. We use an array of resonant-cavity-enhanced photodetectors, each featuring a distinct spectral response in the 850-1700 nm wavelength range. We show that prediction models can be built directly using the responses of the photodetectors, despite the presence of multiple broad peaks, releasing the need for spectral reconstruction. The large etendue and responsivity allow us to demonstrate the application of an integrated near-infrared spectral sensor in relevant problems, namely milk and plastic sensing. Our results open the way to spectral sensors with minimal size, cost and complexity for industrial and consumer applications.

Probing the Nanoscale Heterogeneous Mixing in a High-Performance Polymer Blend

The blend of polyetheretherketone (PEEK) and polybenzimidazole (PBI) produces a high-performance blend (PPB) that is a potential replacement material in several industries due to its high temperature stability and desirable tribological properties. Understanding the nanoscale structure and interface of the two domains of the blend is critical for elucidating the origin of these desirable properties. Whilst achieving the physical characterisation of the domain structures is relatively uncomplicated, the elucidation of structures at the interface presents a significant experimental challenge. In this work, we combine atomic force microscopy (AFM) with an IR laser (AFM-IR) and thermal cantilever probes (nanoTA) to gain insights into the chemical heterogeneity and extent of mixing within the blend structure for the first time. The AFM-IR and nanoTA measurements show that domains in the blend are compositionally different from those of the pure PEEK and PBI polymers, with significant variations observed in a transition region several microns wide in proximity to domain boundary. This strongly points to physical mixing of the two components on a molecular scale at the interface. The versatility intrinsic to the combined methodology employed in this work provides nano- and microscale chemical information that can be used to understand the link between properties of different length scales across a wide range of materials.

Characterizing RNA-binding Ligands on Structures, Chemical Information, Binding Affinity and Drug-likeness

Recent studies suggest RNAs playing essential roles in many cell activities and act as promising drug targets. However, limited development has been achieved in detecting RNA-ligand interactions. To guide the discovery of RNA-binding ligands, it is necessary to characterize them comprehensively. We established a database, RNALID that collects RNA-ligand interactions validated by low-throughput experiment. RNALID contains 358 RNA-ligand interactions. Comparing to other databases, 94.5% of ligands in RNALID are completely or partially novel collections, and 51.78% have novel two-dimensional (2D) structures. The ligand structure analysis indicated that multivalent ligands (MV), ligands binding with cellular mRNA (mRNA), ligands binding with RNA from virus (vRNA) and ligands binding with RNA containing repetitive sequence (rep RNA) are more structurally conserved in both 2D and 3D structures than other ligand types. Binding affinity analysis revealed that interactions between ligands and rep RNA were significantly stronger (two-tailed MW-U test P-value = 0.012) than the interactions between ligands and non-rep RNAs; the interactions between ligands and vRNA were significantly stronger (two-tailed MW-U test P-value = 0.012) than those between ligands and mRNA. Drug-likeness analysis indicated that small molecule (SM) ligands binding with non-rep RNA or vRNA may have higher probability to be drugs than other types of ligands. Comparing ligands in RNALID to FDA-approved drugs and ligands without bioactivity indicated that RNA-binding ligands are different from them in chemical properties, structural properties and drug-likeness. Thus, characterizing the RNA-ligand interactions in RNALID in multiple respects provides new insights into discovering and designing druggable ligands binding with RNA.

Quantitative prediction of moisture content distribution in acetylated wood using near-infrared hyperspectral imaging

The uptake of moisture severely affects the properties of wood in service applications. Even local moisture content variations may be critical, but such variations are typically not detected by traditional methods to quantify the moisture content of the wood. In this study, we used near-infrared hyperspectral imaging to predict the moisture distribution on wood surfaces at the macroscale. A broad range of wood moisture contents were generated by controlling the acetylation degree of wood and the relative humidity during sample conditioning. Near-infrared image spectra were then measured from the surfaces of the conditioned wood samples, and a principal component analysis was applied to separate the useful chemical information from the spectral data. Moreover, a partial least squares regression model was developed to predict moisture content on the wood surfaces. The results show that hyperspectral near-infrared image regression can accurately predict the variations in moisture content across wood surfaces. In addition to sample-to-sample variation in moisture content, our results also revealed differences in the moisture content between earlywood and latewood in acetylated wood. This was in line with our recent studies where we found that thin-walled earlywood cells are acetylated faster than the thicker latewood cells, which decreases the moisture uptake during the conditioning. Dynamic vapor sorption isotherms validated the differences in moisture content within earlywood and latewood cells. Overall, our results demonstrate the capabilities of hyperspectral imaging for process analytics in the modern wood industry. Graphical abstract

Chemical and elemental mapping of spent nuclear fuel sections by soft X-ray spectromicroscopy

Soft X-ray spectromicroscopy at the O K-edge, U N 4,5-edges and Ce M 4,5-edges has been performed on focused ion beam sections of spent nuclear fuel for the first time, yielding chemical information on the sub-micrometer scale. To analyze these data, a modification to non-negative matrix factorization (NMF) was developed, in which the data are no longer required to be non-negative, but the non-negativity of the spectral components and fit coefficients is largely preserved. The modified NMF method was utilized at the O K-edge to distinguish between two components, one present in the bulk of the sample similar to UO2 and one present at the interface of the sample which is a hyperstoichiometric UO2+x species. The species maps are consistent with a model of a thin layer of UO2+x over the entire sample, which is likely explained by oxidation after focused ion beam (FIB) sectioning. In addition to the uranium oxide bulk of the sample, Ce measurements were also performed to investigate the oxidation state of that fission product, which is the subject of considerable interest. Analysis of the Ce spectra shows that Ce is in a predominantly trivalent state, with a possible contribution from tetravalent Ce. Atom probe analysis was performed to provide confirmation of the presence and localization of Ce in the spent fuel.

TXM-Sandbox: an open-source software for transmission X-ray microscopy data analysis

A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook. Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.

Fluorescence Lifetime Phasor Analysis and Raman Spectroscopy of Pigmented Organic Binders and Coatings Used in Artworks

Fluorescence analysis of materials used as binders and coatings in artworks is often hampered by numerous factors, leading to uncertainties in data interpretation. Fluorescence lifetime (FL) measurements enable improvement of the specificity with respect to steady-state measurements by resolving the decay dynamics of the fluorophore emissions. In this work, layers of natural resin, oil, and wax—in pure form, pigmented, in mixtures, and spread on different substrates—were analyzed using a compact, portable, fiber-based FL instrument. FL data were processed via the phasor method and integrated with Raman spectroscopy to obtain complementary chemical information on the different substances. It was observed that the τ-phase of the mixtures is affected by both the pigments and the dispersing medium, and that the presence of the metal substrate contributes to changes in the FL of mixtures. The results obtained with our portable FL system combined with Raman spectroscopy pave the way for a systematic study of a larger number of materials for future in situ applications on works of art.

Neurobiology of phenotypic plasticity in the light of climate change

Phenotypic plasticity describes the ability of an organism with a given genotype to respond to changing environmental conditions through the adaptation of the phenotype. Phenotypic plasticity is a widespread means of adaptation, allowing organisms to optimize fitness levels in changing environments. A core prerequisite for adaptive predictive plasticity is the existence of reliable cues, i.e. accurate environmental information about future selection on the expressed plastic phenotype. Furthermore, organisms need the capacity to detect and interpret such cues, relying on specific sensory signalling and neuronal cascades. Subsequent neurohormonal changes lead to the transformation of phenotype A into phenotype B. Each of these activities is critical for survival. Consequently, anything that could impair an animal’s ability to perceive important chemical information could have significant ecological ramifications. Climate change and other human stressors can act on individual or all of the components of this signalling cascade. In consequence, organisms could lose their adaptive potential, or in the worst case, even become maladapted. Therefore, it is key to understand the sensory systems, the neurobiology and the physiological adaptations that mediate organisms’ interactions with their environment. It is, thus, pivotal to predict the ecosystem-wide effects of global human forcing. This review summarizes current insights on how climate change affects phenotypic plasticity, focussing on how associated stressors change the signalling agents, the sensory systems, receptor responses and neuronal signalling cascades, thereby, impairing phenotypic adaptations.