Showing papers by "San Diego State University published in 2019"

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.

Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis

Abstract: Objective To examine the dose-response associations between accelerometer assessed total physical activity, different intensities of physical activity, and sedentary time and all cause mortality. Design Systematic review and harmonised meta-analysis. Data sources PubMed, PsycINFO, Embase, Web of Science, Sport Discus from inception to 31 July 2018. Eligibility criteria Prospective cohort studies assessing physical activity and sedentary time by accelerometry and associations with all cause mortality and reported effect estimates as hazard ratios, odds ratios, or relative risks with 95% confidence intervals. Data extraction and analysis Guidelines for meta-analyses and systematic reviews for observational studies and PRISMA guidelines were followed. Two authors independently screened the titles and abstracts. One author performed a full text review and another extracted the data. Two authors independently assessed the risk of bias. Individual level participant data were harmonised and analysed at study level. Data on physical activity were categorised by quarters at study level, and study specific associations with all cause mortality were analysed using Cox proportional hazards regression analyses. Study specific results were summarised using random effects meta-analysis. Main outcome measure All cause mortality. Results 39 studies were retrieved for full text review; 10 were eligible for inclusion, three were excluded owing to harmonisation challenges (eg, wrist placement of the accelerometer), and one study did not participate. Two additional studies with unpublished mortality data were also included. Thus, individual level data from eight studies (n=36 383; mean age 62.6 years; 72.8% women), with median follow-up of 5.8 years (range 3.0-14.5 years) and 2149 (5.9%) deaths were analysed. Any physical activity, regardless of intensity, was associated with lower risk of mortality, with a non-linear dose-response. Hazards ratios for mortality were 1.00 (referent) in the first quarter (least active), 0.48 (95% confidence interval 0.43 to 0.54) in the second quarter, 0.34 (0.26 to 0.45) in the third quarter, and 0.27 (0.23 to 0.32) in the fourth quarter (most active). Corresponding hazards ratios for light physical activity were 1.00, 0.60 (0.54 to 0.68), 0.44 (0.38 to 0.51), and 0.38 (0.28 to 0.51), and for moderate-to-vigorous physical activity were 1.00, 0.64 (0.55 to 0.74), 0.55 (0.40 to 0.74), and 0.52 (0.43 to 0.61). For sedentary time, hazards ratios were 1.00 (referent; least sedentary), 1.28 (1.09 to 1.51), 1.71 (1.36 to 2.15), and 2.63 (1.94 to 3.56). Conclusion Higher levels of total physical activity, at any intensity, and less time spent sedentary, are associated with substantially reduced risk for premature mortality, with evidence of a non-linear dose-response pattern in middle aged and older adults. Systematic review registration PROSPERO CRD42018091808.

Quantifying inactive lithium in lithium metal batteries

Abstract: Lithium metal anodes offer high theoretical capacities (3,860 milliampere-hours per gram)1, but rechargeable batteries built with such anodes suffer from dendrite growth and low Coulombic efficiency (the ratio of charge output to charge input), preventing their commercial adoption2,3. The formation of inactive ('dead') lithium- which consists of both (electro)chemically formed Li+ compounds in the solid electrolyte interphase and electrically isolated unreacted metallic Li0 (refs 4,5)-causes capacity loss and safety hazards. Quantitatively distinguishing between Li+ in components of the solid electrolyte interphase and unreacted metallic Li0 has not been possible, owing to the lack of effective diagnostic tools. Optical microscopy6, in situ environmental transmission electron microscopy7,8, X-ray microtomography9 and magnetic resonance imaging10 provide a morphological perspective with little chemical information. Nuclear magnetic resonance11, X-ray photoelectron spectroscopy12 and cryogenic transmission electron microscopy13,14 can distinguish between Li+ in the solid electrolyte interphase and metallic Li0, but their detection ranges are limited to surfaces or local regions. Here we establish the analytical method of titration gas chromatography to quantify the contribution of unreacted metallic Li0 to the total amount of inactive lithium. We identify the unreacted metallic Li0, not the (electro)chemically formed Li+ in the solid electrolyte interphase, as the dominant source of inactive lithium and capacity loss. By coupling the unreacted metallic Li0 content to observations of its local microstructure and nanostructure by cryogenic electron microscopy (both scanning and transmission), we also establish the formation mechanism of inactive lithium in different types of electrolytes and determine the underlying cause of low Coulombic efficiency in plating and stripping (the charge and discharge processes, respectively, in a full cell) of lithium metal anodes. We propose strategies for making lithium plating and stripping more efficient so that lithium metal anodes can be used for next-generation high-energy batteries.

Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005-2017.

Abstract: Drawing from the National Survey on Drug Use and Health (NSDUH; N = 611,880), a nationally representative survey of U.S. adolescents and adults, we assess age, period, and cohort trends in mood disorders and suicide-related outcomes since the mid-2000s. Rates of major depressive episode in the last year increased 52% 2005-2017 (from 8.7% to 13.2%) among adolescents aged 12 to 17 and 63% 2009-2017 (from 8.1% to 13.2%) among young adults 18-25. Serious psychological distress in the last month and suicide-related outcomes (suicidal ideation, plans, attempts, and deaths by suicide) in the last year also increased among young adults 18-25 from 2008-2017 (with a 71% increase in serious psychological distress), with less consistent and weaker increases among adults ages 26 and over. Hierarchical linear modeling analyses separating the effects of age, period, and birth cohort suggest the trends among adults are primarily due to cohort, with a steady rise in mood disorder and suicide-related outcomes between cohorts born from the early 1980s (Millennials) to the late 1990s (iGen). Cultural trends contributing to an increase in mood disorders and suicidal thoughts and behaviors since the mid-2000s, including the rise of electronic communication and digital media and declines in sleep duration, may have had a larger impact on younger people, creating a cohort effect. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Recent Third Pole’s Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis

Abstract: The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research ...

Systematic review of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework

Abstract: Effective implementation of evidence-based practices (EBPs) remains a significant challenge. Numerous existing models and frameworks identify key factors and processes to facilitate implementation. However, there is a need to better understand how individual models and frameworks are applied in research projects, how they can support the implementation process, and how they might advance implementation science. This systematic review examines and describes the research application of a widely used implementation framework, the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. A systematic literature review was performed to identify and evaluate the use of the EPIS framework in implementation efforts. Citation searches in PubMed, Scopus, PsycINFO, ERIC, Web of Science, Social Sciences Index, and Google Scholar databases were undertaken. Data extraction included the objective, language, country, setting, sector, EBP, study design, methodology, level(s) of data collection, unit(s) of analysis, use of EPIS (i.e., purpose), implementation factors and processes, EPIS stages, implementation strategy, implementation outcomes, and overall depth of EPIS use (rated on a 1–5 scale). In total, 762 full-text articles were screened by four reviewers, resulting in inclusion of 67 articles, representing 49 unique research projects. All included projects were conducted in public sector settings. The majority of projects (73%) investigated the implementation of a specific EBP. The majority of projects (90%) examined inner context factors, 57% examined outer context factors, 37% examined innovation factors, and 31% bridging factors (i.e., factors that cross or link the outer system and inner organizational context). On average, projects measured EPIS factors across two of the EPIS phases (M = 2.02), with the most frequent phase being Implementation (73%). On average, the overall depth of EPIS inclusion was moderate (2.8 out of 5). This systematic review enumerated multiple settings and ways the EPIS framework has been applied in implementation research projects, and summarized promising characteristics and strengths of the framework, illustrated with examples. Recommendations for future use include more precise operationalization of factors, increased depth and breadth of application, development of aligned measures, and broadening of user networks. Additional resources supporting the operationalization of EPIS are available.

The fifteenth data release of the Sloan Digital Sky Surveys: First release of MaNGA-derived quantities, data visualization tools, and stellar library

Abstract: Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July–2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA—we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020–2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.

Trends in U.S. Adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print.

Abstract: Studies have produced conflicting results about whether digital media (the Internet, texting, social media, and gaming) displace or complement use of older legacy media (print media such as books, magazines, and newspapers; TV; and movies). Here, we examine generational/time period trends in media use in nationally representative samples of 8th, 10th, and 12th graders in the United States, 1976–2016 (N 1,021,209; 51% female). Digital media use has increased considerably, with the average 12th grader in 2016 spending more than twice as much time online as in 2006, and with time online, texting, and on social media totaling to about 6 hr a day by 2016. Whereas only half of 12th graders visited social media sites almost every day in 2008, 82% did by 2016. At the same time, iGen adolescents in the 2010s spent significantly less time on print media, TV, or movies compared with adolescents in previous decades. The percentage of 12th graders who read a book or a magazine every day declined from 60% in the late 1970s to 16% by 2016, and 8th graders spent almost an hour less time watching TV in 2016 compared with the early 1990s. Trends were fairly uniform across gender, race/ethnicity, and socioeconomic status. The rapid adoption of digital media since the 2000s has displaced the consumption of legacy media.

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Abstract: In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

Atomically engineering activation sites onto metallic 1T-MoS 2 catalysts for enhanced electrochemical hydrogen evolution.

Abstract: Engineering catalytic sites at the atomic level provides an opportunity to understand the catalyst’s active sites, which is vital to the development of improved catalysts. Here we show a reliable and tunable polyoxometalate template-based synthetic strategy to atomically engineer metal doping sites onto metallic 1T-MoS2, using Anderson-type polyoxometalates as precursors. Benefiting from engineering nickel and oxygen atoms, the optimized electrocatalyst shows great enhancement in the hydrogen evolution reaction with a positive onset potential of ~ 0 V and a low overpotential of −46 mV in alkaline electrolyte, comparable to platinum-based catalysts. First-principles calculations reveal co-doping nickel and oxygen into 1T-MoS2 assists the process of water dissociation and hydrogen generation from their intermediate states. This research will expand on the ability to improve the activities of various catalysts by precisely engineering atomic activation sites to achieve significant electronic modulations and improve atomic utilization efficiencies. While heterogeneous catalysts can act as tangible, efficient materials for energy conversion, understanding the active catalytic sites is challenging. Here, authors engineer specific catalytic sites into molybdenum sulfide to improve and elucidate hydrogen evolution electrocatalysis.

Lead halide perovskites for photocatalytic organic synthesis.

Abstract: Nature is capable of storing solar energy in chemical bonds via photosynthesis through a series of C-C, C-O and C-N bond-forming reactions starting from CO2 and light. Direct capture of solar energy for organic synthesis is a promising approach. Lead (Pb)-halide perovskite solar cells reach 24.2% power conversion efficiency, rendering perovskite a unique type material for solar energy capture. We argue that photophysical properties of perovskites already proved for photovoltaics, also should be of interest in photoredox organic synthesis. Because the key aspects of these two applications are both relying on charge separation and transfer. Here we demonstrated that perovskites nanocrystals are exceptional candidates as photocatalysts for fundamental organic reactions, for example C-C, C-N and C-O bond-formations. Stability of CsPbBr3 in organic solvents and ease-of-tuning their bandedges garner perovskite a wider scope of organic substrate activations. Our low-cost, easy-to-process, highly-efficient, air-tolerant and bandedge-tunable perovskites may bring new breakthrough in organic chemistry.

Lead-Halide Perovskites for Photocatalytic α-Alkylation of Aldehydes.

Abstract: Cost-effective and efficient photocatalysis are highly desirable in chemical synthesis. Here we demonstrate that readily prepared suspensions of APbBr3 (A = Cs or methylammonium (MA)) type perovskite colloids (ca. 2–100 nm) can selectively photocatalyze carbon–carbon bond formation reactions, i.e., α-alkylations. Specifically, we demonstrate α-alkylation of aldehydes with a turnover number (TON) of over 52,000 under visible light illumination. Hybrid organic/inorganic perovskites are revolutionizing photovoltaic research and are now impacting other research fields, but their exploration in organic synthesis is rare. Our low-cost, easy-to-process, highly efficient and bandedge-tunable perovskite photocatalyst is expected to bring new insights in chemical synthesis.

Large loss of CO2 in winter observed across the northern permafrost region

Abstract: Recent warming in the Arctic, which has been amplified during the winter1–3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions. Winter warming in the Arctic will increase the CO2 flux from soils. A pan-Arctic analysis shows a current loss of 1,662 TgC per year over the winter, exceeding estimated carbon uptake in the growing season; projections suggest a 17% increase under RCP 4.5 and a 41% increase under RCP 8.5 by 2100.

Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases

Abstract: Molecular clocks are present in almost every cell to anticipate daily recurring and predictable changes, such as rhythmic nutrient availability, and to adapt cellular functions accordingly. At the ...

Olfactory and other sensory impairments in Alzheimer disease.

Abstract: The vast increase in Alzheimer disease (AD) worldwide has grave implications for individuals, family support systems and the health-care systems that will attempt to cope with the disease. Early markers of the disease are essential for efficient selection of clinical trial participants for drug development and for timely treatment once an intervention becomes available. There is avid interest in noninvasive, inexpensive markers that have the potential to identify prodromal AD. This Review considers sensory impairments that have the potential to serve as early indicators of AD, with a focus on olfaction, hearing and vision. Current evidence regarding the potential markers of AD in each modality is examined, with a particular emphasis on olfaction and current findings that olfactory function is associated with prodromal AD. Research suggests that olfactory impairment is associated with other markers that signal the emergence of prodromal AD. Auditory impairment is associated with dementia in epidemiological studies and visual system deficits have been reported in AD; however, the emergence of these deficits in prodromal AD is unclear. Further research is necessary to address the relative sensitivity and specificity of olfactory, auditory and visual measures for the detection of prodromal AD.

Microbes drive global soil nitrogen mineralization and availability.

Abstract: Soil net nitrogen mineralization rate (Nmin ), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil Nmin has not been evaluated on the global scale. By compiling 1565 observational data points of potential net Nmin from 198 published studies across terrestrial ecosystems, we found that Nmin significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of Nmin was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced Nmin through soil microbes. The structural equation models (SEM) showed that soil substrates were the main factors controlling Nmin when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the Nmin prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in Nmin contributed the most to global soil NH4 + -N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on Nmin and highlights the importance of soil microbial biomass in determining Nmin and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.

Less in-person social interaction with peers among U.S. adolescents in the 21st century and links to loneliness:

Abstract: In nationally representative samples of U.S. adolescents (age: 13–18) and entering college students, 1976–2017 (N = 8.2 million), iGen adolescents in the 2010s (vs. previous generations) spent less...

Machine learning in acoustics: Theory and applications

Abstract: Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.

Fetal Alcohol Spectrum Disorders: A Review of the Neurobehavioral Deficits Associated With Prenatal Alcohol Exposure

Abstract: In utero alcohol exposure can disrupt the development of the fetal brain and result in a wide range of neurobehavioral outcomes collectively known as fetal alcohol spectrum disorders (FASD). This paper provides a comprehensive review of the cognitive and behavioral outcomes of prenatal alcohol exposure, including domains of general intelligence, executive functioning, language development, learning and memory, adaptive functioning, academic performance, and concurrent psychopathology. In addition, the current status of the neurobehavioral profile of FASD and its potential as a diagnostic tool will be discussed.

Age- and Sex-Dependent Patterns of Gut Microbial Diversity in Human Adults.

Abstract: Gut microbial diversity changes throughout the human life span and is known to be associated with host sex. We investigated the association of age, sex, and gut bacterial alpha diversity in three large cohorts of adults from four geographical regions: subjects from the United States and United Kingdom in the American Gut Project (AGP) citizen-science initiative and two independent cohorts of Colombians and Chinese. In three of the four cohorts, we observed a strong positive association between age and alpha diversity in young adults that plateaued after age 40 years. We also found sex-dependent differences that were more pronounced in younger adults than in middle-aged adults, with women having higher alpha diversity than men. In contrast to the other three cohorts, no association of alpha diversity with age or sex was observed in the Chinese cohort. The association of alpha diversity with age and sex remained after adjusting for cardiometabolic parameters in the Colombian cohort and antibiotic usage in the AGP cohort. We further attempted to predict the microbiota age in individuals using a machine-learning approach for the men and women in each cohort. Consistent with our alpha-diversity-based findings, U.S. and U.K. women had a significantly higher predicted microbiota age than men, with a reduced difference being seen above age 40 years. This difference was not observed in the Colombian cohort and was observed only in middle-aged Chinese adults. Together, our results provide new insights into the influence of age and sex on the biodiversity of the human gut microbiota during adulthood while highlighting similarities and differences across diverse cohorts. IMPORTANCE Microorganisms in the human gut play a role in health and disease, and in adults higher gut biodiversity has been linked to better health. Since gut microorganisms may be pivotal in the development of microbial therapies, understanding the factors that shape gut biodiversity is of utmost interest. We performed large-scale analyses of the relationship of age and sex to gut bacterial diversity in adult cohorts from four geographic regions: the United States, the United Kingdom, Colombia, and China. In the U.S., U.K., and Colombian cohorts, bacterial biodiversity correlated positively with age in young adults but plateaued at about age 40 years, with no positive association being found in middle-aged adults. Young, but not middle-aged, adult women had higher gut bacterial diversity than men, a pattern confirmed via supervised machine learning. Interestingly, in the Chinese cohort, minimal associations were observed between gut biodiversity and age or sex. Our results highlight the patterns of adult gut biodiversity and provide a framework for future research.

A Misalignment-Tolerant Series-Hybrid Wireless EV Charging System With Integrated Magnetics

Abstract: Electric vehicles (EVs) are becoming increasingly popular as a mean of mitigating issues associated with fossil fuel consumption in transportation systems. A wireless inductive power transfer (IPT) interface between EV and the utility grid has several key advantages, such as safety, convenience, and isolation. However, physical misalignments between the pads of IPT charging systems used in EVs are unavoidable and cause variations in key system parameters, significantly increasing losses and affecting power throughput. This paper presents a novel series-hybrid topology in which the series inductors of the primary and pick-up inductor–capacitor–inductor ( LCL ) networks are integrated into polarized magnetic couplers to improve the system performance under pad misalignment. A mathematical model is developed to investigate the behavior of the proposed system under misalignment. To demonstrate the viability of the proposed method, the results of a 3.3-kW prototype series-hybrid IPT system are presented, benchmarked against a conventional IPT system. Experimental results clearly indicate that the proposed system maintains the output power within ±5% of its rated power despite the pad misalignment. The proposed system is efficient, reliable, and cost effective in comparison to conventional LCL- and CL -compensated IPT systems.

Global phylogeography and ancient evolution of the widespread human gut virus crAssphage

Abstract: Microbiomes are vast communities of microorganisms and viruses that populate all natural ecosystems. Viruses have been considered to be the most variable component of microbiomes, as supported by virome surveys and examples of high genomic mosaicism. However, recent evidence suggests that the human gut virome is remarkably stable compared with that of other environments. Here, we investigate the origin, evolution and epidemiology of crAssphage, a widespread human gut virus. Through a global collaboration, we obtained DNA sequences of crAssphage from more than one-third of the world's countries and showed that the phylogeography of crAssphage is locally clustered within countries, cities and individuals. We also found fully colinear crAssphage-like genomes in both Old-World and New-World primates, suggesting that the association of crAssphage with primates may be millions of years old. Finally, by exploiting a large cohort of more than 1,000 individuals, we tested whether crAssphage is associated with bacterial taxonomic groups of the gut microbiome, diverse human health parameters and a wide range of dietary factors. We identified strong correlations with different clades of bacteria that are related to Bacteroidetes and weak associations with several diet categories, but no significant association with health or disease. We conclude that crAssphage is a benign cosmopolitan virus that may have coevolved with the human lineage and is an integral part of the normal human gut virome.

BNIP3L/NIX and FUNDC1-mediated mitophagy is required for mitochondrial network remodeling during cardiac progenitor cell differentiation

Abstract: Cell-based therapies represent a very promising strategy to repair and regenerate the injured heart to prevent progression to heart failure. To date, these therapies have had limited success due to a lack of survival and retention of the infused cells. Therefore, it is important to increase our understanding of the biology of these cells and utilize this information to enhance their survival and function in the injured heart. Mitochondria are critical for progenitor cell function and survival. Here, we demonstrate the importance of mitochondrial autophagy, or mitophagy, in the differentiation process in adult cardiac progenitor cells (CPCs). We found that mitophagy was rapidly induced upon initiation of differentiation in CPCs. We also found that mitophagy was mediated by mitophagy receptors, rather than the PINK1-PRKN/PARKIN pathway. Mitophagy mediated by BNIP3L/NIX and FUNDC1 was not involved in regulating progenitor cell fate determination, mitochondrial biogenesis, or reprogramming. Instead, mitophagy facilitated the CPCs to undergo proper mitochondrial network reorganization during differentiation. Abrogating BNIP3L- and FUNDC1-mediated mitophagy during differentiation led to sustained mitochondrial fission and formation of donut-shaped impaired mitochondria. It also resulted in increased susceptibility to cell death and failure to survive the infarcted heart. Finally, aging is associated with accumulation of mitochondrial DNA (mtDNA) damage in cells and we found that acquiring mtDNA mutations selectively disrupted the differentiation-activated mitophagy program in CPCs. These findings demonstrate the importance of BNIP3L- and FUNDC1-mediated mitophagy as a critical regulator of mitochondrial network formation during differentiation, as well as the consequences of accumulating mtDNA mutations. Abbreviations: Baf: bafilomycin A1; BCL2L13: BCL2 like 13; BNIP3: BCL2 interacting protein 3; BNIP3L: BCL2 interacting protein 3 like; CPCs: cardiac progenitor cells; DM: differentiation media; DNM1L: dynamin 1 like; EPCs: endothelial progenitor cells; FCCP: carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone; FUNDC1: FUN14 domain containing 1; HSCs: hematopoietic stem cells; MAP1LC3B/LC3: microtubule-associated protein 1 light chain 3 beta; MFN1/2: mitofusin 1/2; MSCs: mesenchymal stem cells; mtDNA: mitochondrial DNA; OXPHOS: oxidative phosphorylation; PPARGC1A: PPARG coactivator 1 alpha; PHB2: prohibitin 2; POLG: DNA polymerase gamma, catalytic subunit; SQSTM1: sequestosome 1; TEM: transmission electron microscopy; TMRM: tetramethylrhodamine methyl ester.

Machine learning in acoustics: theory and applications

Abstract: Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.