Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study’s protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

Solar–thermal water evaporation (SWE) has received much interest in recent years due to a few seminal works on materials innovation and thermal management. With many studies proposing applications like water desalination and sanitization, SWE has become attractive as it can use renewable energy to potentially address pressing water–energy nexus challenges. In this Review, we follow the most researched aspects of SWE indicated by the analytics from text mining the abstracts of papers in this field. We review recent research activities in each aspect and discuss how these studies help improve the overall efficiency of the SWE processes and/or advance their applications, besides pointing out critical deficiencies in the research. We also highlight some interesting findings and inventions emerging from this field. On the basis of the review, we present our perspectives from the scientific and engineering points of view in the hope of providing insights to shape the research direction of the field, promote its further advancement, and eventually help realize the promised applications.

Accurate and reliable quantification of brain metabolites measured in vivo using 1 H magnetic resonance spectroscopy (MRS) is a topic of continued interest in the field. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, we analyze spectrally edited -aminobutyric acid (GABA) MRS data and quantify GABA levels relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using standard GABA+ editing. Unsuppressed water acquisitions from the same volume of interest were acquired for signal referencing. Whole-brain T1-weighted structural images were acquired and tissue-segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA+ measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17%, which was largely driven by vendor-related differences according to a linear mixed-effects analysis. The mean within-site coefficient of variation was 9%. Vendor differences contributed 53% to the total variance in the data, while the remaining variance was attributed to site-(11%) and participant-level (36%) effects. Results from an exploratory analysis suggested that the vendor differences were related to the water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA+ measurements exhibit levels of variance similar to creatine-referenced GABA+ measurements. It is concluded that quantification using internal tissue water referencing remains a viable and reliable method for the in vivo quantification of GABA+ levels.

Open-source data on large scale are the cornerstones for data-driven research, but they are not readily available for polymers. In this work, we build a benchmark database, called PI1M (referring to ∼1 million polymers for polymer informatics), to provide data resources that can be used for machine learning research in polymer informatics. A generative model is trained on ∼12 000 polymers manually collected from the largest existing polymer database PolyInfo, and then the model is used to generate ∼1 million polymers. A new representation for polymers, polymer embedding (PE), is introduced, which is then used to perform several polymer informatics regression tasks for density, glass transition temperature, melting temperature, and dielectric constants. By comparing the PE trained by the PolyInfo data and that by the PI1M data, we conclude that the PI1M database covers similar chemical space as PolyInfo, but significantly populate regions where PolyInfo data are sparse. We believe that PI1M will serve as a good benchmark database for future research in polymer informatics.