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Monitoring the impacts of global efforts to reduce mercury (Hg) emissions is limited by the collection of biological samples at appropriate spatiotemporal scales. This is especially true in the deep sea, a vast region with food webs that cycle bioaccumulative methylmercury (MeHg). Within a species, understanding the distribution of Hg across tissue types can reveal how Hg accumulates in the body and inform how useful a species is for biomonitoring geographic regions or vertical habitats of the ocean. We focus on a globally distributed deep-sea fish, the longnose lancetfish (Alepisaurus ferox, n = 69 individuals), and measure total mercury (THg) and MeHg concentrations in 10 tissue types (brain, caudal white muscle, dorsal white muscle, gallbladder, gill filament, gonad, heart, intestine, liver, and stomach lining). Across all tissue types, THg and MeHg concentrations were higher in large lancetfish (≥1.8 kg) than small lancetfish (<1.8 kg), but concentrations were relatively stable within size classes. THg levels were highest in liver, intestine, and heart, followed by caudal white muscle, dorsal white muscle, stomach lining, and gill filament, then by gonad and gallbladder. We describe how ontogenetic diet shifts explain Hg bioaccumulation in pelagic predators inhabiting similar waters to lancetfish. We hypothesize that diet shifts to deeper-dwelling prey and fishes drive increases in THg and MeHg concentrations in large lancetfish. We propose lancetfish as a strong candidate for monitoring spatiotemporal variability of Hg in the deep pelagic – they are commonly captured in global fisheries and may reflect Hg sources in two distinct vertical habitats of the ocean. 

Abstract Controlling the thermal expansion of ceramic materials is important for many of their applications that involve high-temperature processing and/or working conditions. In this study, we investigate the thermal expansion properties of additively manufactured alumina that is reinforced with boron nitride nanotubes (BNNTs) over a broad temperature range, from room temperature to 900 °C. The coefficient of thermal expansion (CTE) of the BNNT-alumina nanocomposite increases with temperature but decreases with an increase in BNNT loading. The introduction of 0.6% BNNTs results in an approximate 16% reduction in the CTE of alumina. The observed significant CTE reduction of ceramics is attributed to the BNNT’s low CTE and ultrahigh Young’s modulus, and effective interfacial load transfer at the BNNT-ceramic interface. Micromechanical analysis, based onin situRaman measurements, reveals the transition of thermal-expansion-induced interface straining of nanotubes, which shifts from compression to tension inside the ceramic matrix under thermal loadings. This study provides valuable insights into the thermomechanical behavior of BNNT-reinforced ceramic nanocomposites and contributes to the optimal design of ceramic materials with tunable and zero CTE. 

ABSTRACT Multidrug efflux pumps are the frontline defense mechanisms of Gram-negative bacteria, yet little is known of their relative fitness trade-offs under gut conditions such as low pH and the presence of antimicrobial food molecules. Low pH contributes to the proton-motive force (PMF) that drives most efflux pumps. We show how the PMF-dependent pumps AcrAB-TolC, MdtEF-TolC, and EmrAB-TolC undergo selection at low pH and in the presence of membrane-permeant phytochemicals. Competition assays were performed by flow cytometry of co-culturedEscherichia coliK-12 strains possessing or lacking a given pump complex. All three pumps showed negative selection under conditions that deplete PMF (pH 5.5 with carbonyl cyanide 3-chlorophenylhydrazone or at pH 8.0). At pH 5.5, selection against AcrAB-TolC was increased by aromatic acids, alcohols, and related phytochemicals such as methyl salicylate. The degree of fitness cost for AcrA was correlated with the phytochemical’s lipophilicity (logP). Methyl salicylate and salicylamide selected strongly against AcrA, without genetic induction of drug resistance regulons. MdtEF-TolC and EmrAB-TolC each had a fitness cost at pH 5.5, but salicylate or benzoate made the fitness contribution positive. Pump fitness effects were not explained by gene expression (measured by digital PCR). Between pH 5.5 and 8.0,acrAandemrAwere upregulated in the log phase, whereasmdtEexpression was upregulated in the transition-to-stationary phase and at pH 5.5 in the log phase. Methyl salicylate did not affect pump gene expression. Our results suggest that lipophilic non-acidic molecules select against a major efflux pump without inducing antibiotic resistance regulons.IMPORTANCEFor drugs that are administered orally, we need to understand how ingested phytochemicals modulate drug resistance in our gut microbiome. Bacteria maintain low-level resistance by proton-motive force (PMF)-driven pumps that efflux many different antibiotics and cell waste products. These pumps play a key role in bacterial defense by conferring resistance to antimicrobial agents at first exposure while providing time for a pathogen to evolve resistance to higher levels of the antibiotic exposed. Nevertheless, efflux pumps confer energetic costs due to gene expression and pump energy expense. The bacterial PMF includes the transmembrane pH difference (ΔpH), which may be depleted by permeant acids and membrane disruptors. Understanding the fitness costs of efflux pumps may enable us to develop resistance breakers, that is, molecules that work together with antibiotics to potentiate their effect. Non-acidic aromatic molecules have the advantage that they avoid the Mar-dependent induction of regulons conferring other forms of drug resistance. We show that different pumps have distinct selection criteria, and we identified non-acidic aromatic molecules as promising candidates for drug resistance breakers. 

Abstract We quantified cephalopods consumed by longnose lancetfish (Alepisaurus ferox,n = 1267 stomachs containing cephalopod remains) from 2009 to 2018 in the central North Pacific Ocean (between 0–35° N and 135–175° W). When cephalopods identified from beak remains in the stomach contents were included in diet analyses, clear increases in the abundance of gelatinous taxa and the inferred foraging depths of lancetfish were evident. Ontogeny in cephalopod consumption was evident for lancetfish, corroborating past diet studies. Small lancetfish (fork length < 97 cm) fed on smaller, muscular cephalopods from shallow habitats (0–500 m, e.g., Ommastrephidae, Onychoteuthidae), while large lancetfish (fork length ≥ 97 cm) consumed larger, gelatinous cephalopods from deeper waters (depths greater than 500 m, e.g., Amphitretidae, Cranchiidae). Cephalopod beaks were more abundant in the diets of large lancetfish, representing 37.8% of identified cephalopods, numerically. Although beaks likely remain in stomachs longer than soft tissues, they did not simply accumulate with increasing predator size. Cephalopods identified from beaks were also significantly larger than those identified from soft tissues. Despite having low average energy densities, large gelatinous cephalopods are important prey for lancetfish in deep habitats, with energetic values that are comparable to smaller, more muscular cephalopods (95.3 ± 125.8 kJ and 120.2 ± 169.4 kJ, respectively). Holistic consideration of cephalopod beaks in diet analyses will help to elucidate predator foraging behaviors and the trophic and ecological roles of gelatinous cephalopods in deep pelagic food webs. 

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.