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Large amounts of energy storage will be needed to manage renewable-intensive grids and to decarbonize buildings and transportation. Thermal Energy Storage (TES) has the potential to lower first cost while improving round-trip efficiency, safety, and durability compared to lithium batteries. Heating and cooling of buildings consumes 20% of global energy and drives peak electric loads, especially during extreme hot or cold weather. TES can shift heating and cooling loads to off-peak or renewable-intensive periods, thereby reducing grid stress and energy costs during peak periods and supporting renewable integration (by reducing electric consumption during periods when renewable supply is low, and increasing electric consumption when renewable supply is surplus to real-time needs). Integrating TES with heat pumps or chillers can improve efficiency by 20-30% by shifting operation into the heat pump or chiller's optimal efficiency range, and to the optimal time of day based on outdoor temperature (e.g. running the heat pump at night when it is cooler outside and storing cooling for use the next afternoon). In addition to this efficiency benefit, off-peak electricity is often half the price of peak electricity, and load flexibility is a significant benefit to electric utilities in meeting targets for very high reliability and resiliency. This paper reports on early testing of a novel, tunable TES system which uses proprietary materials to store heat and/or cooling at a range of adjustable temperatures optimal for space conditioning in buildings. For example, one can: (a) store heat in winter retuned to store cooling in summer, for 4-seasons markets, as a direct retrofit for chiller/boiler hydronic systems changing to heat pumps, and (b) store cooling in humid weather, at the low temperature required for dehumidification, retuned to store cooling in dry weather, at a more efficient, higher temperature. Tunability significantly reduces the required footprint compared to systems that use non-tunable materials, and provides flexibility to pursue peak load shifting, efficiency improvement, waste heat recovery or enhanced renewable integration as HVAC loads and electricity prices vary. Tunable TES can be applied to large building, campus and district energy applications, and can be used with air-source, ground-source or hybrid heat pump architectures. This paper will report on a recent collaboration between a pre-commercial start-up company and a research organization, where capacity (energy) and rate (power) were measured for stored cooling at 43[degrees]F (6[degrees]C) and stored heat at 150[degrees]F (65.5[degrees]C) at a meaningful lab scale. The research organization provided third-party measurement and verification to validate performance.

Regeneration of myelin is mediated by oligodendrocyte progenitor cells—an abundant stem cell population in the central nervous system (CNS) and the principal source of new myelinating oligodendrocytes. Loss of myelin-producing oligodendrocytes in the CNS underlies a number of neurological diseases, including multiple sclerosis and diverse genetic diseases 1 – 3 . High-throughput chemical screening approaches have been used to identify small molecules that stimulate the formation of oligodendrocytes from oligodendrocyte progenitor cells and functionally enhance remyelination in vivo 4 – 10 . Here we show that a wide range of these pro-myelinating small molecules function not through their canonical targets but by directly inhibiting CYP51, TM7SF2, or EBP, a narrow range of enzymes within the cholesterol biosynthesis pathway. Subsequent accumulation of the 8,9-unsaturated sterol substrates of these enzymes is a key mechanistic node that promotes oligodendrocyte formation, as 8,9-unsaturated sterols are effective when supplied to oligodendrocyte progenitor cells in purified form whereas analogous sterols that lack this structural feature have no effect. Collectively, our results define a unifying sterol-based mechanism of action for most known small-molecule enhancers of oligodendrocyte formation and highlight specific targets to propel the development of optimal remyelinating therapeutics. Many small molecules that stimulate oligodendrocyte formation act not through their canonical pathways but by inhibiting enzymes within the cholesterol biosynthesis pathway and thereby inducing the accumulation of 8,9-unsaturated sterols.

Messenger RNA (mRNA) vaccines represent a new, effective vaccine platform with high capacity for rapid development. Generation of a universal influenza virus vaccine with the potential to elicit long-lasting, broadly cross-reactive immune responses is a necessity for reducing influenza-associated morbidity and mortality. Here we focus on the development of a universal influenza B virus vaccine based on the lipid nanoparticle-encapsulated nucleoside-modified mRNA (mRNA-LNP) platform. We evaluate vaccine candidates based on different target antigens that afford protection against challenge with ancestral and recent influenza B viruses from both antigenic lineages. A pentavalent vaccine combining all tested antigens protects mice from morbidity at a very low dose of 50 ng per antigen after a single vaccination. These findings support the further advancement of nucleoside-modified mRNA-LNPs expressing multiple conserved antigens as universal influenza virus vaccine candidates. The public health concern caused by influenza B virus is often overlooked, yet represents a significant global burden. Here, the authors evaluate the cellular and humoral immune responses of multivalent vaccine candidates, based on the lipid nanoparticle-encapsulated nucleoside-modified mRNA platform, and demonstrate protection of mice from challenge with a broad panel of influenza B viruses.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations. Many microbiome differential abundance methods are available, but it lacks systematic comparison among them. Here, the authors compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups, and show ALDEx2 and ANCOM-II produce the most consistent results.

Profiling microbial community function from metagenomic sequencing data remains a computationally challenging problem. Mapping millions of DNA reads from such samples to reference protein databases requires long run-times, and short read lengths can result in spurious hits to unrelated proteins (loss of specificity). We developed ShortBRED (Short, Better Representative Extract Dataset) to address these challenges, facilitating fast, accurate functional profiling of metagenomic samples. ShortBRED consists of two components: (i) a method that reduces reference proteins of interest to short, highly representative amino acid sequences (\"markers\") and (ii) a search step that maps reads to these markers to quantify the relative abundance of their associated proteins. After evaluating ShortBRED on synthetic data, we applied it to profile antibiotic resistance protein families in the gut microbiomes of individuals from the United States, China, Malawi, and Venezuela. Our results support antibiotic resistance as a core function in the human gut microbiome, with tetracycline-resistant ribosomal protection proteins and Class A beta-lactamases being the most widely distributed resistance mechanisms worldwide. ShortBRED markers are applicable to other homology-based search tasks, which we demonstrate here by identifying phylogenetic signatures of antibiotic resistance across more than 3,000 microbial isolate genomes. ShortBRED can be applied to profile a wide variety of protein families of interest; the software, source code, and documentation are available for download at http://huttenhower.sph.harvard.edu/shortbred.

The yeast Saccharomyces cerevisiae has a long and esteemed history as a model system for laboratory selection experiments. The majority of yeast evolution experiments begin with an isogenic ancestor, impose selection as cells divide asexually, and track mutations that arise and accumulate over time. Within the last decade, the popularity of S. cerevisiae as a model system for exploring the evolution of standing genetic variation has grown considerably. As a facultatively sexual microbe, it is possible to initiate experiments with populations that harbor diversity and also to maintain that diversity by promoting sexual recombination as the experiment progresses. These experimental choices expand the scope of evolutionary hypotheses that can be tested with yeast. And, in this review, I argue that yeast is one of the best model systems for testing such hypotheses relevant to eukaryotic species. Here, I compile a list of yeast evolution experiments that involve standing genetic variation, initially and/or by implementing protocols that induce sexual recombination in evolving populations. I also provide an overview of experimental methods required to set up such an experiment and discuss the unique challenges that arise in this type of research. Throughout the article, I emphasize the best practices emerging from this small but growing niche of the literature.

Antibiotic resistance is a dire clinical problem with important ecological dimensions. While antibiotic resistance in human pathogens continues to rise at alarming rates, the impact of environmental resistance on human health is still unclear. To investigate the relationship between human-associated and environmental resistomes, we analyzed functional metagenomic selections for resistance against 18 clinically relevant antibiotics from soil and human gut microbiota as well as a set of multidrug-resistant cultured soil isolates. These analyses were enabled by Resfams, a new curated database of protein families and associated highly precise and accurate profile hidden Markov models, confirmed for antibiotic resistance function and organized by ontology. We demonstrate that the antibiotic resistance functions that give rise to the resistance profiles observed in environmental and human-associated microbial communities significantly differ between ecologies. Antibiotic resistance functions that most discriminate between ecologies provide resistance to β-lactams and tetracyclines, two of the most widely used classes of antibiotics in the clinic and agriculture. We also analyzed the antibiotic resistance gene composition of over 6000 sequenced microbial genomes, revealing significant enrichment of resistance functions by both ecology and phylogeny. Together, our results indicate that environmental and human-associated microbial communities harbor distinct resistance genes, suggesting that antibiotic resistance functions are largely constrained by ecology.

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use. Complex diseases like cancer and heart disease are caused by the interplay of many factors: the variants of genes we inherit, the lifestyles we lead and the environments we inhabit, plus the interaction of all these factors. In fact, almost every trait, even how many years we will spend studying, is influenced both by our environment and our genes. To identify some of the genetic factors at play, scientists perform analyses known as genome-wide association studies, or GWAS for short. In these studies, the genomes from many different people are scanned to look for genetic differences associated with differences in traits. By summing up all the small genetic differences, so-called “polygenic scores” can be calculated. When there is a large genetic component to a trait, polygenic scores can be useful predictive tools. But there is a catch: polygenic scores make less accurate predictions for individuals of a different ancestry than those involved in the GWAS, which limits the use of these tools around the world. Mostafavi, Harpak et al. set out to understand if there are other factors in addition to ancestry that could influence the performance of polygenic scores. Using data from the UK Biobank, an international health resource that pairs genomic data and clinical information, Mostafavi, Harpak et al. examined polygenic scores among individuals that share a single, common ancestry. These polygenic scores were used to predict three traits (blood pressure, body mass index and educational attainment) in individuals and the predictions were then compared to the actual trait values to see how accurate they were. The analysis revealed that even within a group of people with similar ancestry, the accuracy of polygenic scores can vary, depending on characteristics such as the sex, age or socioeconomic status of the individuals. This analysis emphasises how variable GWAS and their predictive value can be even within seemingly similar population groups. It further highlights both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use in medical and social sciences.

The emergence of divergent SARS-CoV-2 lineages has raised concern that novel variants eliciting immune escape or the ability to displace circulating lineages could emerge within individual hosts. Though growing evidence suggests that novel variants arise during prolonged infections, most infections are acute. Understanding how efficiently variants emerge and transmit among acutely-infected hosts is therefore critical for predicting the pace of long-term SARS-CoV-2 evolution. To characterize how within-host diversity is generated and propagated, we combine extensive laboratory and bioinformatic controls with metrics of within- and between-host diversity to 133 SARS-CoV-2 genomes from acutely-infected individuals. We find that within-host diversity is low and transmission bottlenecks are narrow, with very few viruses founding most infections. Within-host variants are rarely transmitted, even among individuals within the same household, and are rarely detected along phylogenetically linked infections in the broader community. These findings suggest that most variation generated within-host is lost during transmission.

The bacteria that colonize humans and our built environments have the potential to influence our health. Microbial communities associated with seven families and their homes over 6 weeks were assessed, including three families that moved their home. Microbial communities differed substantially among homes, and the home microbiome was largely sourced from humans. The microbiota in each home were identifiable by family. Network analysis identified humans as the primary bacterial vector, and a Bayesian method significantly matched individuals to their dwellings. Draft genomes of potential human pathogens observed on a kitchen counter could be matched to the hands of occupants. After a house move, the microbial community in the new house rapidly converged on the microbial community of the occupants’ former house, suggesting rapid colonization by the family’s microbiota.