UC Santa Barbara

ConspectusThe preparation of discrete and well-defined polymers is an emerging strategy for emulating the remarkable precision achieved by macromolecular synthesis in nature. Although modern controlled polymerization techniques have unlocked access to a cornucopia of materials spanning a broad range of monomers, molecular weights, and architectures, the word controlled is not to be confused with perfect. Indeed, even the highest-fidelity polymerization techniques─yielding molar mass dispersities in the vicinity of Đ = 1.05─unavoidably create a considerable degree of structural and/or compositional dispersity due to the statistical nature of chain growth. Such dispersity impacts many of the properties that researchers seek to control in the design of soft materials.The development of strategies to minimize or entirely eliminate dispersity and access molecularly precise polymers therefore remains a key contemporary challenge. While significant advances have been made in the realm of iterative synthetic methods that construct oligomers with an exact molecular weight, head-to-tail connectivity, and even stereochemistry via small-molecule organic chemistry, as the word iterative suggests, these techniques involve manually propagating monomers one reaction at a time, often with intervening protection and deprotection steps. As a result, these strategies are time-consuming, difficult to scale, and remain limited to lower molecular weights. The focus of this Account is on an alternative strategy that is more accessible to the general scientific community because of its simplicity, versatility, and affordability: chromatography. Researchers unfamiliar with the intricacies of synthesis may recall being exposed to chromatography in an undergraduate chemistry lab. This operationally simple, yet remarkably powerful, technique is most commonly encountered in the purification of small molecules through their selective (differential) adsorption to a column packed with a low-cost stationary phase, usually silica. Because the requisite equipment is readily available and the actual separation takes little time (on the order of 1 h), chromatography is used extensively in small-molecule chemistry throughout industry and academia alike. It is, therefore, perhaps surprising that similar types of chromatography are not more widely leveraged in the field of polymer science as well.Here, we discuss recent advances in using chromatography to control the structure and properties of polymeric materials. Emphasis is placed on the utility of an adsorption-based mechanism that separates polymers based on polarity and composition at tractable (gram) scales for materials science, in contrast to size exclusion, which is extremely common but typically analyzes very small quantities of a sample (∼1 mg) and is limited to separating by molar mass. Key concepts that are highlighted include (1) the separation of low-molecular-weight homopolymers into discrete oligomers (Đ = 1.0) with precise chain lengths and (2) the efficient fractionation of block copolymers into high-quality and widely varied libraries for accelerating materials discovery. In summary, the authors hope to convey the exciting possibilities in polymer science afforded by chromatography as a scalable, versatile, and even automated technique that unlocks new avenues of exploration into well-defined materials for a diverse assortment of researchers with different training and expertise.

Engineered nanomaterials offer numerous benefits to society ranging from environmental remediation to biomedical applications such as drug or vaccine delivery as well as clean and cost-effective energy production and storage, and the promise of a more sustainable way of life. However, as nanomaterials of increasing sophistication enter the market, close attention to potential adverse effects on human health and the environment is needed. Here a critical perspective on nanotoxicological research is provided; the authors argue that it is time to leverage the knowledge regarding the biological interactions of nanomaterials to achieve a more comprehensive understanding of the human health and environmental impacts of these materials. Moreover, it is posited that nanomaterials behave like biological entities and that they should be regulated as such.

INTRODUCTION: The COVID-19 pandemic led countries governments to rapidly establish lockdowns and social distancing, which altered family routines and the quality of family relationships worldwide. OBJECTIVES: This exploratory cross-sectional study aimed to identify the impacts of the social distancing and lockdown in parenting practices of caregivers from Brazil, Mexico, and the USA, and to analyze the continuity of parenting intervention support for children and their families at the beginning of the pandemic in these countries. METHODS: The sample consisted of 704 caregivers of children (286 from Brazil, 225 from Mexico, and 193 from the USA) who answered an online survey about parenting practices before/after quarantine, caregiver/child routines, feelings related to quarantine, changes in everyday life since the beginning of the COVID-19 pandemic, contact with health professionals, and sources of parenting information. RESULTS: Data indicate that caregivers from the three countries experienced similar parenting practices during this time, and did not report significant changes before and after the lockdown. They sought information about parenting predominantly via social media. Those receiving previous mental health care perceived the transition from in-person to telehealth services during the pandemic as feasible and acceptable. CONCLUSION: This study will be helpful for clinicians and parents to contextualize their practices amid long-standing effects that the COVID-19 pandemic can have on children and their families during and post-pandemic from multiple cultural backgrounds.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Salt (NaCl), is an essential nutrient for survival, while excessive salt can be detrimental. In the fruit fly, Drosophila melanogaster, internal taste organs in the pharynx are critical gatekeepers impacting the decision to accept or reject a food. Currently, our understanding of the mechanism through which pharyngeal gustatory receptor neurons (GRNs) sense high salt are rudimentary. Here, we found that a member of the ionotropic receptor family, Ir60b, is expressed exclusively in a pair of GRNs activated by high salt. Using a two-way choice assay (DrosoX) to measure ingestion volume, we demonstrate that IR60b and two co-receptors IR25a and IR76b are required to prevent high salt consumption. Mutants lacking external taste organs but retaining the internal taste organs in the pharynx exhibit much higher salt avoidance than flies with all taste organs but missing the three IRs. Our findings highlight the vital role for IRs in a pharyngeal GRN to control ingestion of high salt.

SIGNIFICANCE: Advances in genetically encoded sensors and two-photon imaging have unlocked functional imaging at the level of single dendritic spines. Synaptic activity can be measured in real time in awake animals. However, tools are needed to facilitate the analysis of the large datasets acquired by the approach. Commonly available software suites for imaging calcium transients in cell bodies are ill-suited for spine imaging as dendritic spines have structural characteristics distinct from those of the cell bodies. We present an automated tuning analysis tool (AUTOTUNE), which provides analysis routines specifically developed for the extraction and analysis of signals from subcellular compartments, including dendritic subregions and spines. AIM: Although the acquisition of in vivo functional synaptic imaging data is increasingly accessible, a hurdle remains in the computation-heavy analyses of the acquired data. The aim of this study is to overcome this barrier by offering a comprehensive software suite with a user-friendly interface for easy access to nonprogrammers. APPROACH: We demonstrate the utility and effectiveness of our software with demo analyses of dendritic imaging data acquired from layer 2/3 pyramidal neurons in mouse V1 in vivo. A user manual and demo datasets are also provided. RESULTS: AUTOTUNE provides a robust workflow for analyzing functional imaging data from neuronal dendrites. Features include source image registration, segmentation of regions-of-interest and detection of structural turnover, fluorescence transient extraction and smoothing, subtraction of signals from putative backpropagating action potentials, and stimulus and behavioral parameter response tuning analyses. CONCLUSIONS: AUTOTUNE is open-source and extendable for diverse functional synaptic imaging experiments. The ease of functional characterization of dendritic spine activity provided by our software can accelerate new functional studies that complement decades of morphological studies of dendrites, and further expand our understanding of neural circuits in health and in disease.

The development of synthetic oligomers as discrete single molecular entities with accurate control over the number and nature of functional groups along the backbone has enabled a variety of new research opportunities. From fundamental studies of self-assembly in materials science to understanding efficacy and safety profiles in biology and pharmaceuticals, future directions are significantly impacted by the availability of discrete, multifunctional oligomers. However, the preparation of diverse libraries of discrete and stereospecific oligomers remains a significant challenge. We report a novel strategy for accelerating the synthesis and isolation of discrete oligomers in a high-throughput manner based on click chemistry and simplified bead-based purification. The resulting synthetic platform allows libraries of discrete polyether oligomers to be prepared and the impact of variables such as chain length, number, and nature of side chain functionalities and molecular dispersity on antibacterial behavior examined. Significantly, discrete oligomers were shown to exhibit enhanced activity with lower toxicity compared with traditional disperse samples. This work provides a practical and scalable methodology for nonexperts to prepare libraries of multifunctional discrete oligomers and demonstrates the advantages of discrete materials in biological applications.

Extending Moores law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.