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The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT + or TH + neurons increases fecal output, while only ChAT + activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain., Competing Interests: Declaration of interests B.B.Y. declares financial interests in Nuanced Health, which is not related to the present study. S.K.M. declares financial interests in Axial Therapeutics and Nuanced Health, which is not related to the present study. P.C.D. is an advisor and holds equity in Cybele and Sirenas and is a scientific co-founder and advisor and holds equity to Ometa, Enveda, and Arome with prior approval by University of California, San Diego. He also consulted for DSM animal health in 2023. R.K. is a scientific advisory board member and consultant for BiomeSense, Inc.; has equity, and receives income. He is a scientific advisory board member of and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc. and has equity. He is a cofounder of Micronoma, has equity, and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

For a Rydberg atom-based sensor to change its sensing frequency, the wavelength of the Rydberg state excitation laser must be altered. The wavelength shifts required can be on the order of 10 nm. A fast-tunable narrow-linewidth laser with broadband tuning capability is required. Here, we present a demonstration of a laser system that can rapidly switch a coupling laser as much as 8 nm in less than 50 μs. The laser system comprises a frequency-stabilized continuous wave laser and an electro-optic frequency comb. A filter enables selection of individual comb lines. A high-speed electro-optic modulator is used to tune the selected comb line to a specific frequency, i.e., an atomic transition. Through Rydberg atom-based sensing experiments, we demonstrate frequency hopping between two Rydberg states and a fast switching time of 400 μs, which we show can be reduced to ∼50 μs with a ping-pong scheme. If updating the RF frequency is not required during frequency hopping, a 200 ns switching time can be achieved. These results showcase the potential of the laser system for advanced Rydberg atom-based radio frequency sensing applications, like communications and radar.

In a large cohort of 1,772 participants from the Hispanic Community Health Study/Study of Latinos with overlapping 16SV4 rRNA gene (bacterial amplicon), ITS1 (fungal amplicon), and shotgun sequencing data, we demonstrate that 16SV4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. In contrast, for fungal taxa, we did not observe meaningful agreements between shotgun and ITS1 amplicon results. Finally, we show that amplicon and shotgun data can be harmonized and pooled to yield larger microbiome datasets with excellent agreement (<1% effect size variance across three independent outcomes) using pooled amplicon/shotgun data compared to pure shotgun metagenomic analysis. Thus, there are multiple approaches to study the microbiome in epidemiological studies, and we provide a demonstration of a powerful pooling approach that will allow researchers to leverage the massive amount of amplicon sequencing data generated over the last two decades., Competing Interests: The authors declare no competing interests.

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment., (© 2022. The Author(s).)

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes., Competing Interests: Declaration of interests G.D.S.-P. and R.K. are inventors on a US patent application (PCT/US2019/059647) submitted by The Regents of the University of California and licensed by Micronoma; that application covers methods of diagnosing and treating cancer using multi-domain microbial biomarkers in blood and cancer tissues. G.D.S.-P. and R.K. are founders of and report stock interest in Micronoma. G.D.S.-P. has filed several additional US patent applications on cancer bacteriome and mycobiome diagnostics that are owned by The Regents of the University of California. R.K. additionally is a member of the scientific advisory board for GenCirq, holds an equity interest in GenCirq, and can receive reimbursements for expenses up to US $5,000 per year. S.W. is an employee of Micronoma. R.S. received a grant from Merck EMD Serono, is a member of the scientific advisory board for Micronoma and reports stock interest in Micronoma, CuResponse, and Biomica, and is a paid adviser to Biomica, CuResponse, and BiomX. R.S., Y.P., I.L., and L.N.-H. are co-inventors on an Israeli provisional patent application (#284860) submitted by Yeda Research and Development, the Weizmann Institute of Science, that covers methods of diagnosing and treating cancer using mycobial biomarkers in cancer tissues., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data. IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

Microbial communities contain a broad phylogenetic diversity of organisms; however, the majority of methods center on describing bacteria and archaea. Fungi are important symbionts in many ecosystems and are potentially important members of the human microbiome, beyond those that can cause disease. To expand our analysis of microbial communities to include data from the fungal internal transcribed spacer (ITS) region, five candidate DNA extraction kits were compared against our standardized protocol for describing bacteria and archaea using 16S rRNA gene amplicon- and shotgun metagenomics sequencing. The results are presented considering a diverse panel of host-associated and environmental sample types and comparing the cost, processing time, well-to-well contamination, DNA yield, limit of detection and microbial community composition among protocols. Across all criteria, the MagMAX Microbiome kit was found to perform best. The PowerSoil Pro kit performed comparably but with increased cost per sample and overall processing time. The Zymo MagBead, NucleoMag Food and Norgen Stool kits were included.

Symbiosis with bacteria is widespread among eukaryotes, including fungi. Bacteria that live within fungal mycelia (endohyphal bacteria) occur in many plant-associated fungi, including diverse Mucoromycota and Dikarya. Pestalotiopsis sp. strain 9143 is a filamentous ascomycete isolated originally as a foliar endophyte of Platycladus orientalis (Cupressaceae). It is infected naturally with the endohyphal bacterium Luteibacter sp. strain 9143, which influences auxin and enzyme production by its fungal host. Previous studies have used transcriptomics to examine similar symbioses between endohyphal bacteria and root-associated fungi such as arbuscular mycorrhizal fungi and plant pathogens. However, currently there are no gene expression studies of endohyphal bacteria of Ascomycota, the most species-rich fungal phylum. To begin to understand such symbioses, we developed methods for assessing gene expression by Pestalotiopsis sp. and Luteibacter sp. when grown in coculture and when each was grown axenically. Our assays showed that the density of Luteibacter sp. in coculture was greater than in axenic culture, but the opposite was true for Pestalotiopsis sp. Dual-transcriptome sequencing (RNA-seq) data demonstrate that growing in coculture modulates developmental and metabolic processes in both the fungus and bacterium, potentially through changes in the balance of organic sulfur via methionine acquisition. Our analyses also suggest an unexpected, potential role of the bacterial type VI secretion system in symbiosis establishment, expanding current understanding of the scope and dynamics of fungal-bacterial symbioses. IMPORTANCE Interactions between microbes and their hosts have important outcomes for host and environmental health. Foliar fungal endophytes that infect healthy plants can harbor facultative endosymbionts called endohyphal bacteria, which can influence the outcome of plant-fungus interactions. These bacterial-fungal interactions can be influential but are poorly understood, particularly from a transcriptome perspective. Here, we report on a comparative, dual-RNA-seq study examining the gene expression patterns of a foliar fungal endophyte and a facultative endohyphal bacterium when cultured together versus separately. Our findings support a role for the fungus in providing organic sulfur to the bacterium, potentially through methionine acquisition, and the potential involvement of a bacterial type VI secretion system in symbiosis establishment. This work adds to the growing body of literature characterizing endohyphal bacterial-fungal interactions, with a focus on a model facultative bacterial-fungal symbiosis in two species-rich lineages, the Ascomycota and Proteobacteria .

Increasing data volumes on high-throughput sequencing instruments such as the NovaSeq 6000 leads to long computational bottlenecks for common metagenomics data preprocessing tasks such as adaptor and primer trimming and host removal. Here, we test whether faster recently developed computational tools (Fastp and Minimap2) can replace widely used choices (Atropos and Bowtie2), obtaining dramatic accelerations with additional sensitivity and minimal loss of specificity for these tasks. Furthermore, the taxonomic tables resulting from downstream processing provide biologically comparable results. However, we demonstrate that for taxonomic assignment, Bowtie2's specificity is still required. We suggest that periodic reevaluation of pipeline components, together with improvements to standardized APIs to chain them together, will greatly enhance the efficiency of common bioinformatics tasks while also facilitating incorporation of further optimized steps running on GPUs, FPGAs, or other architectures. We also note that a detailed exploration of available algorithms and pipeline components is an important step that should be taken before optimization of less efficient algorithms on advanced or nonstandard hardware. IMPORTANCE In shotgun metagenomics studies that seek to relate changes in microbial DNA across samples, processing the data on a computer often takes longer than obtaining the data from the sequencing instrument. Recently developed software packages that perform individual steps in the pipeline of data processing in principle offer speed advantages, but in practice they may contain pitfalls that prevent their use, for example, they may make approximations that introduce unacceptable errors in the data. Here, we show that differences in choices of these components can speed up overall data processing by 5-fold or more on the same hardware while maintaining a high degree of correctness, greatly reducing the time taken to interpret results. This is an important step for using the data in clinical settings, where the time taken to obtain the results may be critical for guiding treatment.

We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Epidemiological studies in adults have shown that exposure to ambient air pollution (AAP) is associated with the composition of the adult gut microbiome, but these relationships have not been examined in infancy. We aimed to determine if 6-month postnatal AAP exposure was associated with the infant gut microbiota at 6 months of age in a cohort of Latino mother-infant dyads from the Southern California Mother's Milk Study (n = 103). We estimated particulate matter (PM 2.5 and PM 10 ) and nitrogen dioxide (NO 2 ) exposure from birth to 6-months based on residential address histories. We characterized the infant gut microbiota using 16S rRNA amplicon sequencing at 6-months of age. At 6-months, the gut microbiota was dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. Our results show that, after adjusting for important confounders, postnatal AAP exposure was associated with the composition of the gut microbiota. As an example, PM 10 exposure was positively associated with Dialister, Dorea, Acinetobacter , and Campylobacter while PM 2.5 was positively associated with Actinomyces . Further, exposure to PM 10 and PM 2.5 was inversely associated with Alistipes and NO 2 exposure was positively associated with Actinomyces, Enterococcus, Clostridium , and Eubacterium . Several of these taxa have previously been linked with systemic inflammation, including the genera Dialister and Dorea . This study provides the first evidence of significant associations between exposure to AAP and the composition of the infant gut microbiota, which may have important implications for future infant health and development.

Background: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting., Methods: We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model., Results: Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic., Conclusions: These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

One goal of microbial ecology researchers is to capture the maximum amount of information from all organisms in a sample. The recent COVID-19 pandemic, caused by the RNA virus SARS-CoV-2, has highlighted a gap in traditional DNA-based protocols, including the high-throughput methods the authors previously established as field standards. To enable simultaneous SARS-CoV-2 and microbial community profiling, the authors compared the relative performance of two total nucleic acid extraction protocols with the authors' previously benchmarked protocol. The authors included a diverse panel of environmental and host-associated sample types, including body sites commonly swabbed for COVID-19 testing. Here the authors present results comparing the cost, processing time, DNA and RNA yield, microbial community composition, limit of detection and well-to-well contamination between these protocols.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

Background: Determining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical-grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients including COVID-19+ patients. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent., Results: Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4× higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic (SYN) swab. The limit of detection (LoD) of SARS-CoV-2 from floor samples collected using the consumer-grade plastic (CGp) or research-grade plastic The Microsetta Initiative (TMI) swabs was similar or better than the SYN swab, further suggesting that swab type does not impact RNA recovery as measured by the abundance of SARS-CoV-2. The LoD for TMI was between 0 and 362.5 viral particles, while SYN and CGp were both between 725 and 1450 particles. Lastly microbiome analyses (16S rRNA gene sequencing) of paired samples (nasal and floor from same patient room) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type, but instead driven by the patient and sample type., Conclusions: Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer-grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity and lack of antibiotics in these samples makes it possible to perform concomitant microbiome analyses. Video abstract.

We aimed to determine if the newborn gut microbiota is an underlying determinant of early life growth trajectories. 132 Hispanic infants were recruited at 1-month postpartum. The infant gut microbiome was characterized using 16S rRNA amplicon sequencing. Rapid infant growth was defined as a weight-for-age z-score (WAZ) change greater than 0.67 between birth and 12-months of age. Measures of infant growth included change in WAZ, weight-for-length z-score (WLZ), and body mass index (BMI) z-scores from birth to 12-months and infant anthropometrics at 12-months (weight, skinfold thickness). Of the 132 infants, 40% had rapid growth in the first year of life. Multiple metrics of alpha-diversity predicted rapid infant growth, including a higher Shannon diversity (OR = 1.83; 95% CI: 1.07-3.29; p = .03), Faith's phylogenic diversity (OR = 1.41, 95% CI: 1.05-1.94; p = .03), and richness (OR = 1.04, 95% CI: 1.01-1.08; p = .02). Many of these alpha-diversity metrics were also positively associated with increases in WAZ, WLZ, and BMI z-scores from birth to 12-months (p all <0.05). Importantly, we identified subsets of microbial consortia whose abundance were correlated with these same measures of infant growth. We also found that rapid growers were enriched in multiple taxa belonging to genera such as Acinetobacter, Collinsella, Enterococcus, Neisseria , and Parabacteroides . Moreover, measures of the newborn gut microbiota explained up to an additional 5% of the variance in rapid growth beyond known clinical predictors (R 2  = 0.37 vs. 0.32, p < .01). These findings indicate that a more mature gut microbiota, characterized by increased alpha-diversity, at as early as 1-month of age, may influence infant growth trajectories in the first year of life.

Lifestyle factors, such as diet, strongly influence the structure, diversity, and composition of the microbiome. While we have witnessed over the last several years a resurgence of interest in fermented foods, no study has specifically explored the effects of their consumption on gut microbiota in large cohorts. To assess whether the consumption of fermented foods is associated with a systematic signal in the gut microbiome and metabolome, we used a multi-omic approach (16S rRNA amplicon sequencing, metagenomic sequencing, and untargeted mass spectrometry) to analyze stool samples from 6,811 individuals from the American Gut Project, including 115 individuals specifically recruited for their frequency of fermented food consumption for a targeted 4-week longitudinal study. We observed subtle but statistically significant differences between consumers and nonconsumers in beta diversity as well as differential taxa between the two groups. We found that the metabolome of fermented food consumers was enriched with conjugated linoleic acid (CLA), a putatively health-promoting molecule. Cross-omic analyses between metagenomic sequencing and mass spectrometry suggest that CLA may be driven by taxa associated with fermented food consumers. Collectively, we found modest yet persistent signatures associated with fermented food consumption that appear present in multiple -omic types which motivate further investigation of how different types of fermented food impact the gut microbiome and overall health. IMPORTANCE Public interest in the effects of fermented food on the human gut microbiome is high, but limited studies have explored the association between fermented food consumption and the gut microbiome in large cohorts. Here, we used a combination of omics-based analyses to study the relationship between the microbiome and fermented food consumption in thousands of people using both cross-sectional and longitudinal data. We found that fermented food consumers have subtle differences in their gut microbiota structure, which is enriched in conjugated linoleic acid, thought to be beneficial. The results suggest that further studies of specific kinds of fermented food and their impacts on the microbiome and health will be useful., (Copyright © 2020 Taylor et al.)

Soil-borne fungi influence coexistence of plant species in mesic environments, but much less is known about their effects on demographic processes relevant to coexistence in arid and semi-arid systems. We isolated 43 fungal strains that naturally colonize seeds of an invasive winter annual (Brassica tournefortii) in the Sonoran Desert, and evaluated the impact of 18 of them on seed germination and mortality of B. tournefortii and a co-occurring native annual (Plantago ovata) under simulated summer and winter temperatures. Fungi isolated from B. tournefortii seeds impacted germination and mortality of seeds of both plant species in vitro. Seed responses reflected host-specific effects by fungi, the degree of which differed significantly between the strains, and depended on the temperature. In the winter temperature, ten fungal strains increased or reduced seed germination, but substantial seed mortality due to fungi was not observed. Two strains increased germination of P. ovata more strongly than B. tournefortii. In the summer temperature, fungi induced both substantial seed germination and mortality, with ten strains demonstrating host-specificity. Under natural conditions, host-specific effects of fungi on seed germination may further differentiate plant species niche in germination response, with a potential of promoting coexistence. Both host-specific and non-host-specific effects of fungi on seed loss may induce polarizing effects on plant coexistence depending on the ecological context. The coexistence theory provides a clear framework to interpret these polarizing effects. Moreover, fungi pathogenic to both plant species could induce host-specific germination, which challenges the theoretical assumption of density-independent germination response. These implications from an in vitro study underscore the need to weave theoretical modeling, reductive empirical experiments, and natural observations to illuminate effects of soil-borne fungi on coexistence of annual plant species in variable desert environments., Competing Interests: The authors have declared that no competing interests exist.

Ultralong-range Rydberg trimer molecules are spectroscopically observed in an ultracold gas of Cs(nd&#95;{3/2}) atoms. The anisotropy of the atomic Rydberg state allows for the formation of angular trimers, whose energies may not be obtained from integer multiples of dimer energies. These nonadditive trimers coexist with Rydberg dimers. The existence of such effective three-body interactions is confirmed with the observation of asymmetric line profiles and interpreted by a theoretical approach that includes relativistic spin interactions. Simulations of the observed spectra with and without angular trimer lines lend convincing support to the existence of effective three-body interactions.

Ultracold molecules formed from association of a single Rydberg atom with surrounding atoms or molecules and those from double Rydberg excitations are discussed in this review. Ultralong-range Rydberg molecules possess a novel molecular bond resulting from scattering of the Rydberg electron from the perturber atoms or molecules. The strong interactions between Rydberg atoms in ultracold gases may lead to formation of macroscopic Rydberg macrodimers. The exquisite control over the properties of the Rydberg electron means that interesting and unusual few-body and quantum many-body features can be realized in such systems.

The ecological modes of fungi are shaped not only by their intrinsic features and the environment in which they occur, but also by their interactions with diverse microbes. Here we explore the ecological and genomic features of diverse bacterial endosymbionts-endohyphal bacteria-that together are emerging as major determinants of fungal phenotypes and plant-fungi interactions. We first provide a historical perspective on the study of endohyphal bacteria. We then propose a functional classification of three main groups, providing an overview of their genomic, phylogenetic, and ecological traits. Last, we explore frontiers in the study of endohyphal bacteria, with special attention to those facultative and horizontally transmitted bacteria that associate with some of the most diverse lineages of fungi. Overall, our aim is to synthesize the rich literature from nearly 50 years of studies on endohyphal bacteria as a means to highlight potential applications and new research directions.

Rydberg atom-based electrometry enables traceable electric field measurements with high sensitivity over a large frequency range, from gigahertz to terahertz. Such measurements are particularly useful for the calibration of radio frequency and terahertz devices, as well as other applications like near field imaging of electric fields. We utilize frequency modulated spectroscopy with active control of residual amplitude modulation to improve the signal to noise ratio of the optical readout of Rydberg atom-based radio frequency electrometry. Matched filtering of the signal is also implemented. Although we have reached similarly, high sensitivity with other read-out methods, frequency modulated spectroscopy is advantageous because it is well-suited for building a compact, portable sensor. In the current experiment, ∼3 µV cm -1 Hz -1/2 sensitivity is achieved and is found to be photon shot noise limited.

Bacterial endosymbionts occur in diverse fungi, including members of many lineages of Ascomycota that inhabit living plants. These endosymbiotic bacteria (endohyphal bacteria, EHB) often can be removed from living fungi by antibiotic treatment, providing an opportunity to assess their effects on functional traits of their fungal hosts. We examined the effects of an endohyphal bacterium ( Chitinophaga sp., Bacteroidetes) on substrate use by its host, a seed-associated strain of the fungus Fusarium keratoplasticum , by comparing growth between naturally infected and cured fungal strains across 95 carbon sources with a Biolog® phenotypic microarray. Across the majority of substrates (62%), the strain harboring the bacterium significantly outperformed the cured strain as measured by respiration and hyphal density. These substrates included many that are important for plant- and seed-fungus interactions, such as D-trehalose, myo -inositol, and sucrose, highlighting the potential influence of EHB on the breadth and efficiency of substrate use by an important Fusarium species. Cases in which the cured strain outperformed the strain harboring the bacterium were observed in only 5% of substrates. We propose that additive or synergistic substrate use by the fungus-bacterium pair enhances fungal growth in this association. More generally, alteration of the breadth or efficiency of substrate use by dispensable EHB may change fungal niches in short timeframes, potentially shaping fungal ecology and the outcomes of fungal-host interactions.

We utilize a homodyne detection technique to achieve a new sensitivity limit for atom-based, absolute radio-frequency electric field sensing of 5 μV cm -1  Hz -1/2 . A Mach-Zehnder interferometer is used for the homodyne detection. With the increased sensitivity, we investigate the dominant dephasing mechanisms that affect the performance of the sensor. In particular, we present data on power broadening, collisional broadening and transit time broadening. Our results are compared to density matrix calculations. We show that photon shot noise in the signal readout is currently a limiting factor. We suggest that new approaches with superior readout with respect to photon shot noise are needed to increase the sensitivity further.

Background: Lipid-lowering drugs are widely underused, despite strong evidence indicating they improve cardiovascular end points. Poor patient adherence to a medication regimen can affect the success of lipid-lowering treatment., Objectives: To assess the effects of interventions aimed at improving adherence to lipid-lowering drugs, focusing on measures of adherence and clinical outcomes., Search Methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, PsycINFO and CINAHL up to 3 February 2016, and clinical trials registers (ANZCTR and ClinicalTrials.gov) up to 27 July 2016. We applied no language restrictions., Selection Criteria: We evaluated randomised controlled trials of adherence-enhancing interventions for lipid-lowering medication in adults in an ambulatory setting with a variety of measurable outcomes, such as adherence to treatment and changes to serum lipid levels. Two teams of review authors independently selected the studies., Data Collection and Analysis: Three review authors extracted and assessed data, following criteria outlined by the Cochrane Handbook for Systematic Reviews of Interventions. We assessed the quality of the evidence using GRADEPro., Main Results: For this updated review, we added 24 new studies meeting the eligibility criteria to the 11 studies from prior updates. We have therefore included 35 studies, randomising 925,171 participants. Seven studies including 11,204 individuals compared adherence rates of those in an intensification of a patient care intervention (e.g. electronic reminders, pharmacist-led interventions, healthcare professional education of patients) versus usual care over the short term (six months or less), and were pooled in a meta-analysis. Participants in the intervention group had better adherence than those receiving usual care (odds ratio (OR) 1.93, 95% confidence interval (CI) 1.29 to 2.88; 7 studies; 11,204 participants; moderate-quality evidence). A separate analysis also showed improvements in long-term adherence rates (more than six months) using intensification of care (OR 2.87, 95% CI 1.91 to 4.29; 3 studies; 663 participants; high-quality evidence). Analyses of the effect on total cholesterol and LDL-cholesterol levels also showed a positive effect of intensified interventions over both short- and long-term follow-up. Over the short term, total cholesterol decreased by a mean of 17.15 mg/dL (95% CI 1.17 to 33.14; 4 studies; 430 participants; low-quality evidence) and LDL-cholesterol decreased by a mean of 19.51 mg/dL (95% CI 8.51 to 30.51; 3 studies; 333 participants; moderate-quality evidence). Over the long term (more than six months) total cholesterol decreased by a mean of 17.57 mg/dL (95% CI 14.95 to 20.19; 2 studies; 127 participants; high-quality evidence). Included studies did not report usable data for health outcome indications, adverse effects or costs/resource use, so we could not pool these outcomes. We assessed each included study for bias using methods described in the Cochrane Handbook for Systematic Reviews of Interventions. In general, the risk of bias assessment revealed a low risk of selection bias, attrition bias, and reporting bias. There was unclear risk of bias relating to blinding for most studies., Authors' Conclusions: The evidence in our review demonstrates that intensification of patient care interventions improves short- and long-term medication adherence, as well as total cholesterol and LDL-cholesterol levels. Healthcare systems which can implement team-based intensification of patient care interventions may be successful in improving patient adherence rates to lipid-lowering medicines.

We calculate vibrational spectra of ultralong-range Cs(32p) Rydberg molecules that form in an ultracold gas of Cs atoms. We account for the partial-wave scattering of the Rydberg electrons from the Cs perturber atoms by including the full set of spin-resolved 1,3 S J and 1,3 P J scattering phase shifts, and allow for the mixing of singlet (S=0) and triplet (S=1) spin states through Rydberg electron spin-orbit and ground state electron hyperfine interactions. Excellent agreement with observed data in Saßmannshausen et al. [Phys. Rev. Lett. 2015, 113, 133201] in line positions and profiles is obtained. We also determine the spin-dependent permanent electric dipole moments for these molecules. This is the first such calculation of ultralong-range Rydberg molecules for which all of the relativistic contributions are accounted., (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We propose a hybrid quantum system where the strong coupling regime can be achieved between a Rydberg atomic ensemble and propagating surface phonon polaritons on a piezoelectric superlattice. By exploiting the large electric dipole moment and long lifetime of Rydberg atoms as well as tightly confined surface phonon polariton modes, it is possible to achieve a coupling constant far exceeding the relevant decay rates. The frequency of the surface mode can be selected so that it is resonant with a Rydberg transition by engineering the piezoelectric superlattice. We describe a way to observe the Rabi splitting associated with the strong coupling regime under realistic experimental conditions. The system can be viewed as a new type of optomechanical system.

Background: Inadequate patient adherence to a medication regimen is a major factor in the lack of success in treating hyperlipidemia. Improved adherence rates may result in significantly improved cardiovascular outcomes in populations treated with lipid-lowering therapy. The purpose of this metaanalysis was to evaluate the effectiveness of interventions aimed at improving adherence to lipid-lowering drugs, focusing on measures of adherence and clinical outcomes., Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PsycINFO, and the Cumulative Index to Nursing and Allied Health Literature databases through January 14, 2015, and also used the results from previous Cochrane reviews of this title. Randomized controlled trials of adherence-enhancing interventions for lipid-lowering medication in adults in an ambulatory setting with measurable outcomes were evaluated with criteria outlined by the Cochrane Handbook for Systematic Reviews of Interventions., Results: Twenty-seven studies randomly assigning 899,068 participants to a variety of interventions were analyzed. One group of interventions categorized as intensified patient care showed significant improvement in adherence rates when compared to usual care (odds ratio 1.93; 95% confidence interval [CI] 1.29-2.88). Additionally, after <6 months of follow-up, total cholesterol decreased by a mean of 17.15 mg/dL (95% CI 1.17-33.14), while after >6 months total cholesterol decreased by a mean of 17.57 mg/dL (95% CI 14.95-20.19)., Conclusion: Healthcare systems that can implement team-based intensified patient care interventions, such as electronic reminders, pharmacist-led interventions, and healthcare professional education of patients, may be successful in improving adherence rates to lipid-lowering medicines.

We investigate the (0001) surface of single crystal quartz with a submonolayer of Rb adsorbates. Using Rydberg atom electromagnetically induced transparency, we investigate the electric fields resulting from Rb adsorbed on the quartz surface, and measure the activation energy of the Rb adsorbates. We show that the adsorbed Rb induces negative electron affinity (NEA) on the quartz surface. The NEA surface allows low energy electrons to bind to the surface and cancel the electric field from the Rb adsorbates. Our results will be important for integrating Rydberg atoms into hybrid quantum systems, as fundamental probes of atom-surface interactions, and for studies of 2D electron gases bound to surfaces.

The observation of strongly interacting many-body phenomena in atomic gases typically requires ultracold samples. Here we show that the strong interaction potentials between Rydberg atoms enable the observation of many-body effects in an atomic vapor, even at room temperature. We excite Rydberg atoms in cesium vapor and observe in real time an out-of-equilibrium excitation dynamics that is consistent with an aggregation mechanism. The experimental observations show qualitative and quantitative agreement with a microscopic theoretical model. Numerical simulations reveal that the strongly correlated growth of the emerging aggregates is reminiscent of soft-matter type systems.

Permanent electric dipole moments are important for understanding symmetry breaking in molecular physics, control of chemical reactions, and realization of strongly correlated many-body quantum systems. However, large molecular permanent electric dipole moments are challenging to realize experimentally. We report the observation of ultralong-range Rydberg molecules with bond lengths of ~100 nanometers and kilo-Debye permanent electric dipole moments that form when an ultracold ground-state cesium (Cs) atom becomes bound within the electronic cloud of an extended Cs electronic orbit. The electronic character of this hybrid class of "trilobite" molecules is dominated by degenerate Rydberg manifolds, making them difficult to produce by conventional photoassociation. We used detailed coupled-channel calculations to reproduce their properties quantitatively. Our findings may lead to progress in ultracold chemistry and strongly correlated many-body physics., (Copyright © 2015, American Association for the Advancement of Science.)

We have recently shown [Nat. Phys.8, 819 (2012)] that Alkali atoms contained in a vapor cell can serve as a highly accurate standard for microwave (MW) electric field strength as well as polarization. Here we show for the first time that Rydberg atom electromagnetically induced transparency can be used to image MW electric fields with unprecedented precision. The spatial resolution of the method is far into the subwavelength regime ∼λ/650 or 66 μm at 6.9 GHz. The electric field resolutions are similar to those we have already demonstrated ∼50  μV cm(-1). Our experimental results agree with finite element calculations of test electric-field patterns.

In comparing multiple treatments, 2 error rates that have been studied extensively are the familywise and false discovery rates. Different methods are used to control each of these rates. Yet, it is rare to find studies that compare the same methods on both of these rates, and also on the per-family error rate, the expected number of false rejections. Although the per-family error rate and the familywise error rate are similar in most applications when the latter is controlled at a conventional low level (e.g., .05), the 2 measures can diverge considerably with methods that control the false discovery rate at that same level. Furthermore, we shall consider both rejections of true hypotheses (Type I errors) and rejections of false hypotheses where the observed outcomes are in the incorrect direction (Type III errors). We point out that power estimates based on the number of correct rejections do not consider the pattern of those rejections, which is important in interpreting the total outcome. The present study introduces measures of interpretability based on the pattern of separation of treatments into nonoverlapping sets and compares methods on these measures. In general, range-based (configural) methods are more likely to obtain interpretable patterns based on treatment separation than individual p-value-based measures. Recommendations for practice based on these results are given in the article. Although the article is complex, these recommendations can be understood without the necessity for detailed perusal of the supporting material.

It is clearly important to pursue atomic standards for quantities like electromagnetic fields, time, length, and gravity. We have recently shown using Rydberg states that Rb atoms in a vapor cell can serve as a practical, compact standard for microwave electric field strength. Here we demonstrate for the first time that Rb atoms excited in a vapor cell can also be used for vector microwave electrometry by using Rydberg-atom electromagnetically induced transparency. We describe the measurements necessary to obtain an arbitrary microwave electric field polarization at a resolution of 0.5°. We compare the experiments to theory and find them to be in excellent agreement.

We observe ultralong-range blueshifted Cs(2) molecular states near ns(1/2) Rydberg states in an optical dipole trap, where 31≤n≤34. The accidental near degeneracy of (n-4)l and ns Rydberg states for l>2 in Cs, due to the small fractional ns quantum defect, leads to nonadiabatic coupling among these states, producing potential wells above the ns thresholds. Two important consequences of admixing high angular momentum states with ns states are the formation of large permanent dipole moments, ~15-100 Debye, and accessibility of these states via two-photon association. The observed states are in excellent agreement with theory.

We present a locking circuit for external cavity diode lasers implemented on a field-programmable gate array (FPGA). The main advantages over traditional non-FPGA-based locking circuits are rapid reconfigurability without any soldering and a friendly user interface. We characterize the lock quality by measuring the linewidth of a locked laser using electromagnetically induced transparency in a Rb vapor cell.

In a combined experimental and theoretical effort we report on two novel types of ultracold long-range Rydberg molecules. First, we demonstrate the creation of triatomic molecules of one Rydberg atom and two ground-state atoms in a single-step photoassociation. Second, we assign a series of excited dimer states that are bound by a so far unexplored mechanism based on internal quantum reflection at a steep potential drop. The properties of the Rydberg molecules identified in this work qualify them as prototypes for a new type of chemistry at ultracold temperatures.

In this work, we study the role of the ac Stark effects on the excitation of nS1/2 cold Rydberg atoms produced in a rubidium magneto-optical trap. We have observed an atomic population in the nP3/2 state after excitation of nS1/2 for 29

Rydberg atoms have an electron in a state with a very high principal quantum number, and as a result can exhibit unusually long-range interactions. One example is the bonding of two such atoms by multipole forces to form Rydberg-Rydberg molecules with very large internuclear distances. Notably, bonding interactions can also arise from the low-energy scattering of a Rydberg electron with negative scattering length from a ground-state atom. In this case, the scattering-induced attractive interaction binds the ground-state atom to the Rydberg atom at a well-localized position within the Rydberg electron wavefunction and thereby yields giant molecules that can have internuclear separations of several thousand Bohr radii. Here we report the spectroscopic characterization of such exotic molecular states formed by rubidium Rydberg atoms that are in the spherically symmetric s state and have principal quantum numbers, n, between 34 and 40. We find that the spectra of the vibrational ground state and of the first excited state of the Rydberg molecule, the rubidium dimer Rb(5s)-Rb(ns), agree well with simple model predictions. The data allow us to extract the s-wave scattering length for scattering between the Rydberg electron and the ground-state atom, Rb(5s), in the low-energy regime (kinetic energy, <100 meV), and to determine the lifetimes and the polarizabilities of the Rydberg molecules. Given our successful characterization of s-wave bound Rydberg states, we anticipate that p-wave bound states, trimer states and bound states involving a Rydberg electron with large angular momentum-so-called trilobite molecules-will also be realized and directly probed in the near future.

Multiple test procedures are usually compared on various aspects of error control and power. Power is measured as some function of the number of false hypotheses correctly identified as false. However, given equal numbers of rejected false hypotheses, the pattern of rejections, i.e. the particular set of false hypotheses identified, may be crucial in interpreting the results for potential application.In an important area of application, comparisons among a set of treatments based on random samples from populations, two different approaches, cluster analysis and model selection, deal implicitly with such patterns, while traditional multiple testing procedures generally focus on the outcomes of subset and pairwise equality hypothesis tests, without considering the overall pattern of results in comparing methods. An important feature involving the pattern of rejections is their relevance for dividing the treatments into distinct subsets based on some parameter of interest, for example their means. This paper introduces some new measures relating to the potential of methods for achieving such divisions. Following Hartley (1955), sets of treatments with equal parameter values will be called clusters. Because it is necessary to distinguish between clusters in the populations and clustering in sample outcomes, the population clusters will be referred to as P -clusters; any related concepts defined in terms of the sample outcome will be referred to with the prefix outcome. Outcomes of multiple comparison procedures will be studied in terms of their probabilities of leading to separation of treatments into outcome clusters, with various measures relating to the number of such outcome clusters and the proportion of true vs. false outcome clusters. The definitions of true and false outcome clusters and related concepts, and the approach taken here, is in the tradition of hypothesis testing with attention to overall error control and power, but with added consideration of cluster separation potential.The pattern approach will be illustrated by comparing two methods with apparent FDR control but with different ways of ordering outcomes for potential significance: The original Benjamini-Hochberg (1995) procedure (BH), and the Newman-Keuls (Newman, 1939; Keuls, 1952) procedure (NK).