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    Can artificially altered clouds save the Great Barrier Reef?

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    In place of its normal load of cars and vans, the repurposed ferry boat sported a mobile science laboratory and a large fan on its deck as it left Townsville, Australia, in March. Researchers dropped anchor in a coral lagoon some 100 kilometres offshore and then fired up the cone-shaped turbine, which blew a mist of seawater off the back of the boat. What happened next came as a welcome surprise: after briefly drifting along the ocean surface, the plume ascended into the sky.Looking a bit like a jet engine, this mist machine is at the centre of an experiment that, if successful, could help to determine the future of the Great Barrier Reef. Three-hundred and twenty nozzles spewed a cloud of nano-sized droplets engineered to brighten clouds and block sunlight — providing a bit of cooling shade for the coral colonies below. Scientists used sensors aboard the ferry, drones and a second boat to monitor the plume as it migrated skyward.The experiment wasn’t big enough to significantly alter the clouds. But preliminary results from the field tests — which were shared exclusively with Nature — suggest that the technology might perform even better than computer models suggested it would, says Daniel Harrison, an oceanographer and engineer at Southern Cross University in Coffs Harbour, Australia, who is heading up the research. “We are now very confident that we can get the particles up into the clouds,” Harrison says. “But we still need to figure out how the clouds will respond.”Harrison’s project is the world’s first field trial of marine cloud brightening, one of several controversial geoengineering technologies that scientists have studied in the laboratory for decades. The research has been driven by fear that humans might one day be forced to deliberately manipulate the Earth’s climate and weather systems to blunt the most severe impacts of global warming.For many Australians, that day arrived in 2017, when a marine heat wave spurred massive coral bleaching and death across much of the 2,300-kilometre Great Barrier Reef. That crisis hit just a year after another bleaching event along the reef, which supports more than 600 species of coral and an estimated 64,000 jobs in industries such as tourism and fishing. Research suggests that the reef lost more than half of its coral between 1995 and 2017, as a result of warming waters, tropical storms and predatory starfish (A. Dietzel et al. Proc. R. Soc. B. 287, 20201432; 2020).
    These corals could survive climate change — and help save the world’s reefs
    The project has raised concerns among some scientists abroad, in part because the Australian group has published little about its work. Environmentalists outside Australia objected to the project last year after news of the first trial broke, and there could be similar criticism when details of the 2021 trial emerge.Harrison stresses that the cloud-brightening project is about local adaptation to climate change, not global geoengineering, because its application would be limited in both space and time. It’s also just one part of a larger Aus$300 million (US$220 million) Reef Restoration and Adaptation Program (RRAP) launched last year by Australia to investigate and develop techniques and technologies to save the country’s reefs. Many of the proposals, from cloud brightening to breeding heat-tolerant corals, would represent unprecedented human interventions in the natural reef system.Ecological modelling suggests that a large-scale intervention involving multiple strategies — including a fleet of mist machines — could prolong the life of the reef while governments work to eliminate greenhouse-gas emissions. The goal now is to work out what’s achievable in the real world, says Cedric Robillot, executive director of the RRAP.“You need to consider every angle, from the fundamental science to the very pointy end of engineering, if you want to succeed,” Robillot says. “It’s not enough to just prove you could do it. You need to explain how you would do it.”Into the cloudsHarrison conducted his first field test in March 2020: a three-day proof-of-concept expedition on a small car ferry with four scientists, one representative from a local Indigenous group, and two shipping containers for equipment and sleeping quarters. The team had a minimal Aus$400,000 budget and limited scientific instrumentation to monitor the mist, but it was enough to document that the plume flowing out of their mist machine rode a draught of warm air high into the sky.It was the first time they had witnessed this phenomenon. Their models had suggested that evaporation of the brine droplets would cool the plume, which would then float across the surface of the ocean, only slowly mixing upwards into the low-lying marine clouds. The models also indicated a risk that the tiny droplets might merge and drop out of the air. Instead, brine droplets floated along the surface of the ocean for half a kilometre without coalescing, gradually losing water and weight to evaporation along the way. And then they shot upwards.

    A marine heat wave in 2017 caused coral bleaching along much of Australia’s Great Barrier Reef.Credit: Juergen Freund/Nature Picture Library

    “We didn’t expect that at all,” Harrison says, “but it turned out we were doing this experiment in the middle of a rising air mass.”The scientists feared it was a fluke. Although years of research and development have gone into the nozzles, initially led by a separate American team, this was the first time anybody had ever deployed them in the field with fresh seawater. The team also didn’t know what to expect from clouds and aerosols in that region, because research on the reef has focused almost exclusively on what happens below the water, not the conditions above.For Harrison, the 2020 experiment was more than enough to justify moving forward with another, larger trial in March 2021. But it did raise eyebrows among some scientists and observers abroad, where geoengineering research has met strong opposition and struggled to attract funding.
    IPCC climate report: Earth is warmer than it’s been in 125,000 years
    Most of the concern has centred on a form of solar geoengineering that involves injecting reflective material into the stratosphere to block sunlight at a global scale. But cloud brightening has also been studied as a potential global intervention, and it has attracted criticism from some environmental groups who argue that it carries inevitable ecological risks and detracts from efforts to limit greenhouse gases.Some scientists, as well as environmental advocates who follow geoengineering research, told Nature that they were surprised to see the experiment move forward without more scrutiny — or without published research to justify such an investment.Critics also worry that Australia is setting the wrong kind of precedent by rebranding a solar-geoengineering experiment that could have regional impacts as a local adaptation project. “One could say that there should have been some level of consultation with the outside world,” says Janos Pasztor, who heads the Carnegie Climate Governance Initiative, an advocacy group in New York City that has been pushing for a global debate over geoengineering governance in the United Nations.Harrison says scientists in the programme have consulted with regulatory authorities, as well as with the general public and Indigenous groups that have historic claims on the reef. He also readily acknowledges trying to avoid getting embroiled in a debate about solar geoengineering, arguing that the project would be more akin to cloud-seeding operations that are designed to promote rain and that are not considered to be geoengineering. One of the next modelling efforts, however, will be to explore any potential regional and global implications, he says.

    A plume of seawater droplets rises up into the sky during a field trial in March 2021.Credit: Brendan Kelaher/SCU

    Others question the Australian government’s motivations in funding such work. Under the conservative prime minister Scott Morrison, the government has yet to strengthen its climate pledge under the 2015 Paris agreement, as many nations have done in the past year. Morrison has personally ruled out committing to net-zero emissions. Pushing for a technological fix to global warming without moving to aggressively curb greenhouse gases is “sheer lunacy”, says Peter Frumhoff, chief climate scientist for the Union of Concerned Scientists, an advocacy group in Cambridge, Massachusetts.Some researchers, however, are pleased to see marine cloud brightening move from theory to the field, including US scientists working on a similar project that has been struggling to get into the field for nearly a decade. “This is an early example of how climate disruption can drive interest in these things,” says Sarah Doherty, an atmospheric physicist who manages the Marine Cloud Brightening Project at the University of Washington in Seattle. Members of the team provided the initial nozzle design and have been tracking the Australian group’s progress.Coral crisisThe first time that scientists observed a major bleaching event along the Great Barrier Reef was in 1998, and the second event followed four years later. In both cases, corals expelled the algae that live within them and that provide colour and energy through photosynthesis. Most of the corals eventually recovered. But in 2016 and 2017, many corals bleached and then died across two-thirds of the reef.
    First sun-dimming experiment will test a way to cool Earth
    “It was absolutely horrifying,” says David Wachenfeld, chief scientist at the Great Barrier Reef Marine Park Authority, which manages the reef. The clear message from those events was that the traditional approach to managing corals and coral reefs would not be enough, he adds. “Our hand was forced.”In 2018, the Australian government allocated Aus$6 million to a consortium of universities and government research institutes for a feasibility study focused on potentially radical strategies that could be applied across the reef. Researchers reviewed some 160 ideas, including putting live corals on ice for long-term preservation and synthetically engineeering new varieties that can tolerate the warmer waters. Many approaches proved too costly and energy intensive, but 43 interventions were singled out for further study. Marine cloud brightening drew support in part because it theoretically provides direct relief precisely when and where corals need it most.Much of the emphasis of the programme is on helping corals to adapt and repopulate the reef, including efforts to improve coral aquaculture operations so that they can produce millions of corals per year rather than thousands. For Madeleine van Oppen, a coral geneticist at the Australian Institute of Marine Science near Townsville, the RRAP programme helps to integrate her team’s work on assisting coral evolution to make them more heat tolerant.Thanks to the RRAP, she says, data from those projects are now being fed directly into models that enable researchers to assess the potential benefits — as well as the risks — of releasing new strains of coral and microalga into the wild. The programme is also raising ecological questions, such as whether the introduction of new coral species can propagate disease, or whether a new variety of more heat-tolerant corals might displace corals struggling to survive.

    Researchers are testing specialized nozzles that create jets of seawater mist.Credit: Alejandro Tagliafico/SCU

    “It sort of speeds up the whole path from research to implementation in the field,” says van Oppen.In the long run, the models indicate that without interventions, the extent of coral on the reef could shrink by well over 60% by 2070 compared with 2020 levels (S. A. Condie et al. R. Soc. Open Sci. 8, 201296; 2021). But simulations suggest that Australia could cut those losses in half with a three-pronged approach focused on propagating heat-tolerant corals, controlling outbreaks of the predatory crown-of-thorns starfish and brightening clouds to take the edge off of heat waves. Crucially, the latest modelling also suggests that without the cooling provided by Harrison’s cloud brightening project, the other interventions might not amount to much.Testing the windWhen Harrison’s group returned to the field this year, they had more-powerful drones as well as other aerosol sensors on a second boat. As in the previous year’s experiment, each time they created a plume, it rose into the sky after the droplets lost around 90% of their water to evaporation. The likely explanation, Harrison says, is that the reef is creating its own weather as warm water along the shallow corals heats the air above.Many more droplets are making it into the clouds than the scientists had initially calculated, but Harrison says their mist machine might need to be scaled up by a factor of 10 — from 320 to around 3,000 nozzles — to produce enough particles to brighten nearby clouds by around 30%. His team’s modelling suggests that this could in turn reduce the incoming solar radiation on the reef locally by around 6.5%. Even then, the operation would require 800–1,000 stations to cover the length of the Great Barrier Reef.
    Fevers are plaguing the oceans — and climate change is making them worse
    But it’s unclear whether that spray of salty droplets will have the desired effect, says Lynn Russell, an atmospheric chemist at the Scripps Institution of Oceanography in La Jolla, California, who has studied cloud brightening. Russell has not seen the latest — and as-yet unpublished — results, but questions whether there are enough of the low layered clouds considered suitable for cloud brightening.Harrison acknowledges such concerns and says that his team sees more of these clouds on the southern part of the reef. His team’s modelling suggests the technology will also work on the clouds that are common across the rest of the reef in summer. Even then, he says, it remains unclear how much coverage a full-scale cloud-brightening operation could provide across the entirety of the reef. More measurements, and detailed modelling, are needed to provide answers.For now, Harrison has secured funding for another two years, and he needs to demonstrate progress. The RRAP is testing all 43 approaches and will redistribute resources to projects that show potential, Robillot says. But he stresses that no amount of science and engineering will preserve the reef in its current form. “Even if we do all of this, the system that you’ll end up with is not going to be the Great Barrier Reef that we know today,” Robillot says. “You might, however, retain a very functional ecosystem.”That’s enough to keep Harrison going, and his team is already preparing for a trip into the field in 2022. The scientists plan to run the mist machine at higher pressure, which should produce a sixfold increase in the number of particles, and they will use new instrumentation to determine how particles alter clouds. They are also investigating an entirely different nozzle technology that could reduce the number of nozzles needed by a factor of 1,000.Harrison is more confident today than he was even a year ago that cloud brightening might work over the reef, but he is also realistic about the future if governments fail to limit carbon emissions. “There are only so many clouds available, and there is only so much you can brighten them,” he says. “Eventually, climate change just overwhelms things.”

    Nature 596, 476-478 (2021)
    doi: https://doi.org/10.1038/d41586-021-02290-3

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    Migrations of cancer cells through the lens of phylogenetic biogeography

    Conceptualization of organismal to tumor biogeography through clone phylogeniesWe first map organismal biogeographic concepts and models to the process of migration and colonization of cancer cells during metastasis. Tumors are populations consisting of a diversity of cancer cells with different genetic profiles that may represent different lineages in the clone phylogeny. We use the example in Fig. 1, which contains a phylogeny of 17 clones found in one primary tumor (P) and four metastases (M1–M4). Events occurring along a branch in a phylogeny are anagenetic events, which include diversification, extinction, and expansion12,14. In organismal evolutionary biology, anagenetic events are not directly observed except through the fossil record. However, one can map the collection of genetic variants that likely arose on individual lineage in a phylogeny. In many cancers, sequencing of temporally sampled biopsies’ can directly reveal anagenetic events similar to the sequencing of ancient DNA in paleogenomics.The other types of evolutionary events in the phylogeny are cladogenetic, including genetic divergences and dispersals (Fig. 1). Genetic differences observed among species and populations are the key to detect cladogenetic events reconstructed in molecular phylogenies of living descendants. In cancer, temporal sampling of biopsies can reveal cladogenetic events that produced extinct descendants.In biogeography, genetic divergence results in the diversification of lineages within an area. Sometimes, the term duplication is used, but we avoid its further use because of the confusion it may cause in evolutionary genomics. Divergence events are also observed in a clone phylogeny, particularly when clone lineages diverge from each other within a tumor or across tumors. The exact opposite of genetic diversification can also be observed when lineages partially or fully disappear from the phylogeny. Extinction can occur due to random chance, selection, or environmental pressures. Even though extinction is rarely discussed in tumor clone phylogenetics, it happens frequently.Phylogenies also reveal movements of lineages between locations (geographic areas or body parts) when the locations of individual cells, species, or populations are known5,6,7,8,9,15. When lineages accumulate genetic differences along a branch in the phylogeny, and the evolved lineages migrate to a new area, we observe an expansion event. Expansions differ from dispersals in such that the growth of a population occurs in the same place. This movement of cells of a clone from one location to another, where they would potentially form a metastasis, results in the dispersal of  these cells of that clone to additional areas, which is modeled by a dispersal rate (d) in organismal biogeography. When a clone genetically diverges following its migration, then a distant dispersal event is said to have occurred. Similarly, when a clone diverges from the rest of the clones within a tumor and disperses to another tumor, we have observed an expansion event. Thus, clone phylogenies can give insights into the origin and trajectory of cancer cells between tumors.When a clone is no longer present at a location, it is extinct at that location. Extinctions are modeled by an extinction rate (e) in biogeographic models. As a result of extinction, the range of descendent clones on a phylogeny can be smaller than the ancestors. Biogeography models also have a parameter (J) to consider founder events that establish new populations from a few individuals. In phylogenies, founder events can be detected if only one or a few cells are found to have moved from one location to another to start diversifying in a new area. Both distant dispersal and founder events may result in forming a new colony of cells, i.e., a new metastasis in the case of cancer cell migrations. The primary distinction between dispersal and founder events is the relative number of migrating cells. Founder events are due to one or a few cells, whereas dispersal events involve a larger number of migrating cells. Founder events are expected to be more common in tumor evolution because metastases are thought to be formed by the spread of only one or a few cancer cells. These biogeographic events have been mathematically modeled and implemented in various approaches to infer species migration events12, which are directly applicable in the inference of cancer cell migrations between tumors.Model fitsWe began by analyzing the statistical fits of six biogeographic models (Table 1) to 80 computer-simulated tumor evolutionary datasets. Simulations enable us to assess the performance of computational approaches and reveal potential caveats associated with their use because the ground truth is known. These datasets were simulated using four main clone migration schemes defined by the different number of migrating clones (1–3), the small and large number of tumor areas (5–7 tumors, m5 datasets; 8–11 tumors, m8 datasets), and the different types of source areas of migration (primary or metastasis). The following seeding scenarios reflect this complexity of the clone migration schemes: monoclonal single-source seeding (mS), polyclonal single-source seeding (pS), polyclonal multisource seeding (pM), and polyclonal reseeding (pR) (see “Methods” section).Table 1 Phylogenetic and biogeographic events considered in seven biogeographic models used for analysis.Full size tableWe considered biogeographic models that weigh genetic divergence, dispersal/expansion, and extinction events differently (Table 1). We also explored the provision of including founder events in our models on the accuracy of detecting clone migrations. The parameterization of the aforementioned events results in models with two free parameters, i.e., dispersal rate (d) and extinction rate (e), and models with three free parameters by adding the founder-event speciation (J); see “Methods” section for more details.Overall, we tested six biogeographic models for their fit to the tumor data, three models with two free parameters and three others with three free parameters. BAYAREALIKE, DEC, and DIVALIKE models have two parameters each. They are nested within their respective models that add the founder effect, resulting in a model with three free parameters (hereinafter +J models). We used the BioGeoBEARS software for all model fit analyses. In data analysis, we first inferred phylogeny of cancer cell populations (clone phylogeny) using an existing method16, followed by the use of BioGeoBEARS to infer the clone migration history in which the clone phylogeny is used along with the location of tumor sites in which each clone is observed (Fig. 2). BioGeoBEARS estimates the probabilities of annotating internal nodes with tumor locations. These annotations are then used to derive cancer cell migration paths when two adjacent nodes are annotated with different tumor locations. In these analyses, we assumed the correct clone phylogeny because our focus was not assessing the impact of errors in a phylogeny on the accuracy of clone migration inferences. We also compared the accuracy of migration histories reconstructed using biogeographic models in BioGeoBEARS with those obtained from the approaches that do not model biogeographic processes (BBM9, MACHINA5, and PathFinder7).Figure 2Data analysis pipeline using BioGeoBEARS in R14 to infer clonal migration histories.Full size imageWe first conducted Likelihood Ratio Tests (LRTs) to examine the improvement offered by considering founder events in modeling tumor migrations. In this case, the fit of the BAYAREALIKE, DEC, and DIVALIKE models was compared to their +J counterparts, respectively. The null hypothesis was rejected for more than 50% of the datasets (BAYAREALIKE: 71.25%, DEC: 60%, and DIVALIKE: 53.75%; P  More

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    T6SS secretes an LPS-binding effector to recruit OMVs for exploitative competition and horizontal gene transfer

    The Fur-regulated T6SS1 plays an important role in iron acquisition in C. necator
    To explore the function of T6SS1 (Reut_A1713 to Reut_A1733) in C. necator (Fig. S1A), we analyzed the T6SS1 promoter and identified a Fur binding site (AGAAATA) upstream of gene reut_A1733. This Fur binding site was highly similar to the Fur-box reported in E. coli [38], with a probability score of 2.25 (out of a maximum score = 2.45) (Fig. S1B), which was calculated by applying the position weight matrix to a sequence [39]. Incubation of the T6SS1 promoter probe with purified Fur protein led to decreased mobility of the probe in the electrophoretic mobility shift assay, suggesting a direct interaction between Fur and the T6SS1 promoter (Fig. 1A). To further determine the function of Fur on the expression of T6SS1, a single-copy PT6SS1::lacZ fusion reporter was introduced into the chromosomes of C. necator wild-type (WT), Δfur deletion mutant, and the Δfur(fur) complementary strain. Compared to WT, the PT6SS1::lacZ promoter activity was significantly increased in the Δfur mutant (about 2.2-fold), and this increase could be restored by introducing the complementary plasmid pBBR1MCS-5-fur (Fig. 1B). Similar results were obtained by analyzing the expression of T6SS1 core component genes (hcp1, clpV1, vgrG1, and tssM1) with qRT-PCR (Fig. S1C). These results demonstrate that the expression of T6SS1 in C. necator is directly repressed by Fur, the master regulator of genes involved in iron homeostasis in many prokaryotes [40, 41].Fig. 1: Regulation of T6SS1 expression by Fur.A The interactions between His6-Fur and the T6SS1 promoter examined by EMSA. Increasing amounts of Fur (0, 0.03, 0.06, 0.13, 0.25, and 1.0 μM) and 10 nM DNA fragments were used in the assay. A 500 bp unrelated DNA fragment (Control A) and 1 µM BSA (Control B) were included in the assay as negative controls. B Fur represses the expression of T6SS1. β-galactosidase activities of T6SS1 promoter from chromosomal lacZ fusions in relevant C. necator strains were measured. C Iron uptake requires T6SS1. Stationary-phase C. necator strains were washed twice with M9 medium. Iron associated with indicated bacterial cells were measured with ICP-MS. The vector corresponds to the plasmid pBBR1MCS-5 (B) and pBBR1MCS-2 (C), respectively. Data are represented as mean values ± SD of three biological replicates, each with three technical replicates. **p  More

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    Publisher Correction: Principles, drivers and opportunities of a circular bioeconomy

    AffiliationsAnimal Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsAbigail Muscat, Evelien M. de Olde, Raimon Ripoll-Bosch & Imke J. M. de BoerFarming Systems Ecology group, Wageningen University & Research, Wageningen, The NetherlandsHannah H. E. Van ZantenPublic Administration and Policy group, Wageningen University & Research, Wageningen, The NetherlandsTamara A. P. Metze & Catrien J. A. M. TermeerPlant Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsMartin K. van IttersumAuthorsAbigail MuscatEvelien M. de OldeRaimon Ripoll-BoschHannah H. E. Van ZantenTamara A. P. MetzeCatrien J. A. M. TermeerMartin K. van IttersumImke J. M. de BoerCorresponding authorCorrespondence to
    Imke J. M. de Boer. More

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    Non-lethal effects of entomopathogenic nematode infection

    1.Gaugler, R. Entomopathogenic nematology (2002).2.Gaugler, R. Entomopathogenic Nematodes in Biological Control (CRC Press, 2018).Book 

    Google Scholar 
    3.Grewal, P. S., Ehlers, R.-U. & Shapiro-Ilan, D. I. Nematodes as Biocontrol Agents (CABI, 2005).Book 

    Google Scholar 
    4.Duncan, L. & McCoy, C. Vertical distribution in soil, persistence, and efficacy against citrus root weevil (coleoptera: Curculionidae) of two species of entomogenous nematodes (rhabditida: Steinernematidae; heterorhabditidae). Environ. Entomol. 25, 174–178 (1996).Article 

    Google Scholar 
    5.Duncan, L., McCoy, C. & Terranova, A. Estimating sample size and persistence of entomogenous nematodes in sandy soils and their efficacy against the larvae of Diaprepes abbreviatus in Florida. J. Nematol. 28, 56 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Bullock, R., Pelosi, R. & Killer, E. Management of citrus root weevils (coleoptera: Curculionidae) on florida citrus with soil-applied entomopathogenic nematodes (nematoda: Rhabditida). Florida Entomologist 1–7 (1999).7.Koppenhöfer, A. M. & Fuzy, E. M. Steinernema scarabaei for the control of white grubs. Biol. Control 28, 47–59 (2003).Article 

    Google Scholar 
    8.Grewal, P., Power, K., Grewal, S., Suggars, A. & Haupricht, S. Enhanced consistency in biological control of white grubs (coleoptera: Scarabaeidae) with new strains of entomopathogenic nematodes. Biol. Control 30, 73–82 (2004).Article 

    Google Scholar 
    9.Georgis, R. et al. Successes and failures in the use of parasitic nematodes for pest control. Biol. Control 38, 103–123 (2006).Article 

    Google Scholar 
    10.Labaude, S. & Griffin, C. T. Transmission success of entomopathogenic nematodes used in pest control. Insects 9, 72 (2018).Article 

    Google Scholar 
    11.Li, X.-Y., Cowles, R., Cowles, E., Gaugler, R. & Cox-Foster, D. Relationship between the successful infection by entomopathogenic nematodes and the host immune response. Int. J. Parasitol. 37, 365–374 (2007).CAS 
    Article 

    Google Scholar 
    12.Castillo, J. C., Reynolds, S. E. & Eleftherianos, I. Insect immune responses to nematode parasites. Trends Parasitol. 27, 537–547 (2011).CAS 
    Article 

    Google Scholar 
    13.Ribeiro, C. et al. Insect immunity-effects of factors produced by a nematobacterial complex on immunocompetent cells. J. Insect Physiol. 45, 677–685 (1999).CAS 
    Article 

    Google Scholar 
    14.Garriga, A., Mastore, M., Morton, A., Garcia del Pino, F. & Brivio, M. F. Immune response of drosophila suzukii larvae to infection with the nematobacterial complex steinernema carpocapsae-xenorhabdus nematophila. Insects 11, 210 (2020).Article 

    Google Scholar 
    15.Ebrahimi, L., Niknam, G., Dunphy, G. & Toorchi, M. Side effects of immune response of colorado potato beetle, leptinotarsa decemlineata against the entomopathogenic nematode, steinernema carpocapsae infection. Invertebr. Surviv. J. 11, 132–142 (2014).
    Google Scholar 
    16.Ebrahimi, L., Niknam, G. & Lewis, E. Lethal and sublethal effects of iranian isolates of steinernema feltiae and heterorhabditis bacteriophora on the colorado potato beetle, leptinotarsa decemlineata. Biocontrol 56, 781–788 (2011).Article 

    Google Scholar 
    17.Chen, S., Li, J., Han, X. & Moens, M. Effect of temperature on the pathogenicity of entomopathogenic nematodes (Steinernema and Heterorhabditis spp.) to delia radicum. Biocontrol 48, 713–724 (2003).Article 

    Google Scholar 
    18.Mastore, M., Quadroni, S., Toscano, A., Mottadelli, N. & Brivio, M. F. Susceptibility to entomopathogens and modulation of basal immunity in two insect models at different temperatures. J. Therm. Biol 79, 15–23 (2019).CAS 
    Article 

    Google Scholar 
    19.Wojda, I. Temperature stress and insect immunity. J. Therm. Biol 68, 96–103 (2017).CAS 
    Article 

    Google Scholar 
    20.Lee, J. H., Dillman, A. R. & Hallem, E. A. Temperature-dependent changes in the host-seeking behaviors of parasitic nematodes. BMC Biol. 14, 1–17 (2016).Article 

    Google Scholar 
    21.Girling, R., Ennis, D., Dillon, A. & Griffin, C. The lethal and sub-lethal consequences of entomopathogenic nematode infestation and exposure for adult pine weevils, Hylobius abietis (coleoptera: Curculionidae). J. Invertebr. Pathol. 104, 195–202 (2010).CAS 
    Article 

    Google Scholar 
    22.Mastore, M., Arizza, V., Manachini, B. & Brivio, M. F. Modulation of immune responses of Rhynchophorus ferrugineus (insecta: Coleoptera) induced by the entomopathogenic nematode Steinernema carpocapsae (nematoda: Rhabditida). Insect Sci. 22, 748–760 (2015).CAS 
    Article 

    Google Scholar 
    23.Willett, D. S., Filgueiras, C. C., Nyrop, J. P. & Nault, B. A. Attract and kill: spinosad containing spheres to control onion maggot (Delia antiqua). Pest Manag. Sci. 76, 2720–2725 (2020).CAS 
    Article 

    Google Scholar 
    24.Willett, D. S., Filgueiras, C. C., Nyrop, J. P. & Nault, B. A. Field monitoring of onion maggot (Delia antiqua) fly through improved trapping. J. Appl. Entomol. 144, 382–387 (2020).Article 

    Google Scholar 
    25.Kaya, H. K. & Stock, S. P. Techniques in insect nematology. In Manual of Techniques in Insect Pathology, 281–324 (Elsevier, 1997).26.White, G. et al. A method for obtaining infective nematode larvae from cultures. Science (Washington) 66, 302–303 (1927).ADS 
    CAS 
    Article 

    Google Scholar 
    27.R Core Team. R: A. Language and Environment for Statistical Computing. R Foundation for Statistical Computing (Vienna, Austria, 2021).28.Wickham, H. et al. Welcome to the tidyverse. J. Open Sour. Softw. 4, 1686 (2019). https://doi.org/10.21105/joss.01686ADS 
    Article 

    Google Scholar 
    29.Fox, J. & Weisberg, S. An R Companion to Applied Regression third. (Sage, 2019).
    Google Scholar 
    30.Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means (2021). R package version 1.5.5-1.31.Franceschi, C. et al. Genes involved in immune response/inflammation, igf1/insulin pathway and response to oxidative stress play a major role in the genetics of human longevity: The lesson of centenarians. Mech. Ageing Dev. 126, 351–361 (2005).CAS 
    Article 

    Google Scholar 
    32.Kumar, S. et al. Lifespan extension in C. elegans caused by bacterial colonization of the intestine and subsequent activation of an innate immune response. Dev. Cell 49, 100–117 (2019).CAS 
    Article 

    Google Scholar 
    33.Bruno, P. et al. Entomopathogenic nematodes from Mexico that can overcome the resistance mechanisms of the western corn rootworm. Sci. Rep. 10, 1–12 (2020).Article 

    Google Scholar 
    34.Stock, S. P., Campos-Herrera, R., El-Borai, F. & Duncan, L. Steinernema khuongi n. sp. (panagrolaimomorpha, steinernematidae), a new entomopathogenic nematode species from Florida, USA. J. Helminthol. 93, 226–241 (2019).CAS 
    Article 

    Google Scholar 
    35.Nagelkerke, N. J. et al. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).MathSciNet 
    Article 

    Google Scholar  More

  • in

    Nano/microparticles in conjunction with microalgae extract as novel insecticides against Mealworm beetles, Tenebrio molitor

    1.Köhler, H. R. & Triebskorn, R. Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond?. Science 341(6147), 759–765 (2013).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    2.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418(6898), 671–677 (2002).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Khan, M. N., Mobin, M., Abbas, Z. K., AlMutairi, K. A. & Siddiqui, Z. H. Role of nanomaterials in plants under challenging environments. Plant Physiol. Biochem. 110, 194–209 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Monica, R. C. & Cremonini, R. Nanoparticles and higher plants. Caryologia 62(2), 161–165 (2009).Article 

    Google Scholar 
    5.Zheng, L., Hong, F., Lu, S. & Liu, C. Effect of nano-TiO2 on strength of naturally aged seeds and growth of spinach. Biol. Trace Elem. Res. 104(1), 83–91 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Lin, D. & Xing, B. Phytotoxicity of nanoparticles: inhibition of seed germination and root growth. Environ. Pollut. 150(2), 243–250 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Kah, M. Nanopesticides and nanofertilizers: emerging contaminants or opportunities for risk mitigation?. Front. Chem. 3, 64 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Sirelkhatim, A. et al. Review on zinc oxide nanoparticles: antibacterial activity and toxicity mechanism. Nano-micro letters 7(3), 219–242 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Selvarajan, V., Obuobi, S. & Ee, P. L. R. Silica Nanoparticles—A Versatile Tool for the Treatment of Bacterial Infections. Front. Chem. 8, 602 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Lykov, A. et al. Silica Nanoparticles as a Basis for Efficacy of Antimicrobial Drugs. Nanostruct. Antimicrob. Therapy 1, 551–575 (2017).Article 

    Google Scholar 
    11.Kim, J. S. et al. Antimicrobial effects of silver nanoparticles. Nanomed. Nanotechnol. Biol. Med. 3(1), 95–101 (2007).CAS 
    Article 

    Google Scholar 
    12.Sharma, A., Patni, B., Shankhdhar, D. & Shankhdhar, S. C. Zinc–an indispensable micronutrient. Physiol. Mol. Biol. Plants 19(1), 11–20 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Kawachi, M. et al. A mutant strain Arabidopsis thaliana that lacks vacuolar membrane zinc transporter MTP1 revealed the latent tolerance to excessive zinc. Plant Cell Physiol. 50(6), 1156–1170 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Yan, A. & Chen, Z. Impacts of silver nanoparticles on plants: a focus on the phytotoxicity and underlying mechanism. Int. J. Mol. Sci. 20(5), 1003 (2019).CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    15.Vigneron, A., Jehan, C., Rigaud, T. & Moret, Y. Immune defenses of a beneficial pest: the mealworm beetle Tenebrio molitor. Front. Physiol. 10, 138 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Renukadevi, K. P., Saravana, P. S. & Angayarkanni, J. Antimicrobial and antioxidant activity of Chlamydomonas reinhardtii sp. Int. J. Pharm. Sci. Res. 2(6), 1467 (2011).
    Google Scholar 
    17.Jayshree, A., Jayashree, S. & Thangaraju, N. Chlorella vulgaris and Chlamydomonas reinhardtii: effective antioxidant, antibacterial and anticancer mediators. Indian J. Pharm. Sci. 78(5), 575–581 (2016).CAS 
    Article 

    Google Scholar 
    18.Kamble, P., Cheriyamundath, S., Lopus, M. & Sirisha, V. L. Chemical characteristics, antioxidant and anticancer potential of sulfated polysaccharides from Chlamydomonas reinhardtii. J. Appl. Phycol. 30(3), 1641–1653 (2018).CAS 
    Article 

    Google Scholar 
    19.Vishwakarma, J., Parmar, V. & Vavilala, S. L. Nitrate stress-induced bioactive sulfated polysaccharides from Chlamydomonas reinhardtii. Biomed. Res. J. 6(1), 7 (2019).
    Google Scholar 
    20.Burghardt, M., Schreiber, L. & Riederer, M. Enhancement of the diffusion of active ingredients in barley leaf cuticular wax by monodisperse alcohol ethoxylates. J. Agric. Food Chem. 46(4), 1593–1602 (1998).CAS 
    Article 

    Google Scholar 
    21.Henderson, C. F. & Tilton, E. W. Tests with acaricides against the brown wheat mite. J. Econ. Entomol. 48(2), 157–161 (1955).CAS 
    Article 

    Google Scholar 
    22.Debnath, N. et al. Entomotoxic effect of silica nanoparticles against Sitophilus oryzae (L.). J. Pest Sci. 84(1), 99–105 (2011).Article 

    Google Scholar 
    23.Aktar, M. W., Sengupta, D. & Chowdhury, A. Impact of pesticides use in agriculture: their benefits and hazards. Interdiscip. Toxicol. 2(1), 1 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Majumder, D. D. et al. Current status and future trends of nanoscale technology and its impact on modern computing, biology, medicine and agricultural biotechnology. In 2007 International Conference on Computing: Theory and Applications (ICCTA’07), 563–573 (2007).25.Rahman, A. et al. Surface functionalized amorphous nanosilica and microsilica with nanopores as promising tools in biomedicine. Naturwissenschaften 96(1), 31–38 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Pérez-de-Luque, A. & Rubiales, D. Nanotechnology for parasitic plant control. Pest Manag. Sci.: Formerly Pesticide Sci. 65(5), 540–545 (2009).Article 
    CAS 

    Google Scholar 
    27.Chakravarthy, A. K. et al. Bio efficacy of inorganic nanoparticles CdS, Nano-Ag and Nano-TiO2 against Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). Current Biotica 6(3), 271–281 (2012).
    Google Scholar 
    28.Benelli, G. Mode of action of nanoparticles against insects. Environ. Sci. Pollut. Res. 25(13), 12329–12341 (2018).CAS 
    Article 

    Google Scholar 
    29.Karthiga, P., Rajeshkumar, S. & Annadurai, G. Mechanism of larvicidal activity of antimicrobial silver nanoparticles synthesized using Garcinia mangostana bark extract. J. Cluster Sci. 29(6), 1233–1241 (2018).CAS 
    Article 

    Google Scholar 
    30.Rouhani, M., Samih, M. A. & Kalantari, S. Insecticide effect of silver and zinc nanoparticles against Aphis nerii Boyer De Fonscolombe (Hemiptera: Aphididae). Chil. J. Agric. Res. 72(4), 590 (2012).Article 

    Google Scholar 
    31.Rouhani, M., Samih, M. A. & Kalantari, S. Insecticidal effect of silica and silver nanoparticles on the cowpea seed beetle, Callosobruchus maculatus F(Col: Bruchidae). J. Entomol. Res. 4(4), 297–305 (2013).
    Google Scholar 
    32.Sabbour, M. M. Entomotoxicity assay of two nanoparticle materials 1-(Al2O3 and TiO2) against Sitophilus oryzae under laboratory and store conditions in Egypt. J. Novel Appl. Sci. 1(4), 103–108 (2012).
    Google Scholar 
    33.Stadler, T., Buteler, M. & Weaver, D. K. Novel use of nanostructured alumina as an insecticide. Pest Manag. Sci.: Formerly Pesticide Sci. 66(6), 577–579 (2010).CAS 
    Article 

    Google Scholar 
    34.Xu, R. ISO International standards for particle sizing. China Particuol. 2(4), 164–167 (2004).CAS 
    Article 

    Google Scholar 
    35.Lee, Y. S., Kang, M. H., Cho, S. Y. & Jeong, C. S. Effects of constituents of Amomum xanthioides on gastritis in rats and on growth of gastric cancer cells. Arch. Pharmacal Res. 30(4), 436–443 (2007).CAS 
    Article 

    Google Scholar 
    36.Hussein, H. A. et al. Phytochemical screening, metabolite profiling and enhanced antimicrobial activities of microalgal crude extracts in co-application with silver nanoparticle. Bioresour. Bioprocess. 7(1), 1–17 (2020).MathSciNet 
    Article 

    Google Scholar 
    37.Jeevanandam, J., Barhoum, A., Chan, Y. S., Dufresne, A. & Danquah, M. K. Review on nanoparticles and nanostructured materials: history, sources, toxicity and regulations. Beilstein J. Nanotechnol. 9(1), 1050–1074 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Servin, A. et al. A review of the use of engineered nanomaterials to suppress plant disease and enhance crop yield. J. Nanopart. Res. 17(2), 1–21 (2015).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    39.Barik, T. K., Kamaraju, R. & Gowswami, A. Silica nanoparticle: a potential new insecticide for mosquito vector control. Parasitol. Res. 111(3), 1075–1083 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Gao, Y. et al. Thermoresponsive polymer-encapsulated hollow mesoporous silica nanoparticles and their application in insecticide delivery. Chem. Eng. J. 383, 1269 (2020).
    Google Scholar 
    41.Debnath, N., Das, S., Patra, P., Mitra, S. & Goswami, A. Toxicological evaluation of entomotoxic silica nanoparticle. Toxicol. Environ. Chem. 94(5), 944–951 (2012).CAS 
    Article 

    Google Scholar 
    42.Debnath, N., Mitra, S., Das, S. & Goswami, A. Synthesis of surface functionalized silica nanoparticles and their use as entomotoxic nanocides. Powder Technol. 221, 252–256 (2012).CAS 
    Article 

    Google Scholar 
    43.Chang, J. S., Chang, K. L. B., Hwang, D. F. & Kong, Z. L. In vitro cytotoxicitiy of silica nanoparticles at high concentrations strongly depends on the metabolic activity type of the cell line. Environ. Sci. Technol. 41(6), 2064–2068 (2007).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Gogos, A., Knauer, K. & Bucheli, T. D. Nanomaterials in plant protection and fertilization: current state, foreseen applications, and research priorities. J. Agric. Food Chem. 60(39), 9781–9792 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Mondal, K. K. & Mani, C. Investigation of the antibacterial properties of nanocopper against Xanthomonas axonopodis pv punicae, the incitant of pomegranate bacterial blight. Ann. Microbiol. 62(2), 889–893 (2012).CAS 
    Article 

    Google Scholar 
    46.Norman, D. J. & Chen, J. Effect of foliar application of titanium dioxide on bacterial blight of geranium and Xanthomonas leaf spot of poinsettia. HortScience 46(3), 426–428 (2011).CAS 
    Article 

    Google Scholar 
    47.Salem, H. F., Kam, E. & Sharaf, M. A. Formulation and evaluation of silver nanoparticles as antibacterial and antifungal agents with a minimal cytotoxic effect. Int. J. Drug Deliv. 3(2), 293 (2011).CAS 

    Google Scholar 
    48.Lamsa, K. et al. Inhibition effects of silver nanoparticles against powdery mildews on cucumber and pumpkin. Mycobiology 39(1), 26–32 (2011).Article 
    CAS 

    Google Scholar 
    49.Schofield, R. M. S. Metals in cuticular structures. Scorp. Biol. Res. 1, 234–256 (2001).
    Google Scholar 
    50.Oonincx, D. G. A. B. & Van der Poel, A. F. B. Effects of diet on the chemical composition of migratory locusts (Locusta migratoria). Zoo Biol. 30(1), 9–16 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Van Broekhoven, S., Oonincx, D. G., Van Huis, A. & Van Loon, J. J. Growth performance and feed conversion efficiency of three edible mealworm species (Coleoptera: Tenebrionidae) on diets composed of organic by-products. J. Insect Physiol. 73, 1–10 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    52.Locke, M. & Nichol, H. Iron economy in insects: transport, metabolism, and storage. Annu. Rev. Entomol. 37(1), 195–215 (1992).CAS 
    Article 

    Google Scholar 
    53.Jones, M. W., de Jonge, M. D., James, S. A. & Burke, R. Elemental mapping of the entire intact Drosophila gastrointestinal tract. J. Biol. Inorg. Chem. 20(6), 979–987 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Mir, A. H., Qamar, A., Qadir, I., Naqvi, A. H. & Begum, R. Accumulation and trafficking of zinc oxide nanoparticles in an invertebrate model, Bombyx mori, with insights on their effects on immuno-competent cells. Sci. Rep. 10(1), 1–14 (2020).Article 
    CAS 

    Google Scholar 
    55.Zhang, X. F., Shen, W. & Gurunathan, S. Silver nanoparticle-mediated cellular responses in various cell lines: an in vitro model. Int. J. Mol. Sci. 17(10), 1603 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    56.Liau, S. Y., Read, D. C., Pugh, W. J., Furr, J. R. & Russell, A. D. Interaction of silver nitrate with readily identifiable groups: relationship to the antibacterialaction of silver ions. Lett. Appl. Microbiol. 25(4), 279–283 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Matsumura, Y., Yoshikata, K., Kunisaki, S. I. & Tsuchido, T. Mode of bactericidal action of silver zeolite and its comparison with that of silver nitrate. Appl. Environ. Microbiol. 69(7), 4278–4281 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Gupta, A., Maynes, M. & Silver, S. Effects of halides on plasmid-mediated silver resistance in Escherichia coli. Appl. Environ. Microbiol. 64(12), 5042–5045 (1998).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Lee, J. H. et al. Biopersistence of silver nanoparticles in tissues from Sprague-Dawley rats. Part. Fibre Toxicol. 10(1), 1–14 (2013).Article 
    CAS 

    Google Scholar 
    60.Vinluan, R. D. III. & Zheng, J. Serum protein adsorption and excretion pathways of metal nanoparticles. Nanomedicine 10(17), 2781–2794 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Armstrong, N., Ramamoorthy, M., Lyon, D., Jones, K. & Duttaroy, A. Mechanism of silver nanoparticles action on insect pigmentation reveals intervention of copper homeostasis. PLoS ONE 8(1), 53186 (2013).ADS 
    Article 
    CAS 

    Google Scholar 
    62.Chun, J. P., Choi, J. S. & Ahn, Y. J. Utilization of fruit bags coated with nano-silver for controlling black stain on fruit skin of ‘niitaka’pear (Pyrus pyrifolia). Hortic. Environ. Biotechnol. 51(4), 245–248 (2010).
    Google Scholar 
    63.Jo, Y. K., Kim, B. H. & Jung, G. Antifungal activity of silver ions and nanoparticles on phytopathogenic fungi. Plant Dis. 93(10), 1037–1043 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Comparing recurrent convolutional neural networks for large scale bird species classification

    1.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Inger, R. et al. Common European birds are declining rapidly while less abundant species numbers are rising. Ecol. Lett. 18, 28–36 (2015).Article 

    Google Scholar 
    3.Leach, E. C., Burwell, C. J., Ashton, L. A., Jones, D. N. & Kitching, R. L. Comparison of point counts and automated acoustic monitoring: Detecting birds in a rainforest biodiversity survey. Emu 116, 305–309 (2016).Article 

    Google Scholar 
    4.Drake, K. L., Frey, M., Hogan, D. & Hedley, R. Using digital recordings and sonogram analysis to obtain counts of yellow rails. Wildl. Soc. Bull. 40, 346–354 (2016).Article 

    Google Scholar 
    5.Lambert, K. T. & McDonald, P. G. A low-cost, yet simple and highly repeatable system for acoustically surveying cryptic species. Austral. Ecol. 39, 779–785 (2014).Article 

    Google Scholar 
    6.Burnett, K. Distribution, abundance, and acoustic characteristics of Kohala forest birds. Ph.D. thesis, University of Hawaii at Hilo (2020).7.Owen, K. et al. Bioacoustic analyses reveal that bird communities recover with forest succession in tropical dry forests. Avian Conserv. Ecol. 15, 25 (2020).Article 

    Google Scholar 
    8.Furnas, B. J., Landers, R. H. & Bowie, R. C. Wildfires and mass effects of dispersal disrupt the local uniformity of type I songs of hermit warblers in California. Auk 137, ukaa031 (2020).Article 

    Google Scholar 
    9.Aide, T. M. et al. Real-time bioacoustics monitoring and automated species identification. PeerJ 1, e103 (2013).Article 

    Google Scholar 
    10.Potamitis, I., Ntalampiras, S., Jahn, O. & Riede, K. Automatic bird sound detection in long real-field recordings: Applications and tools. Appl. Acoust. 80, 1–9 (2014).Article 

    Google Scholar 
    11.Stowell, D. & Plumbley, M. D. Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ 2, e488 (2014).Article 

    Google Scholar 
    12.Tachibana, R. O., Oosugi, N. & Okanoya, K. Semi-automatic classification of birdsong elements using a linear support vector machine. PLoS ONE 9, e92584 (2014).ADS 
    Article 

    Google Scholar 
    13.Zheng, A. & Casari, A. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (OReilly, London, 2018).
    Google Scholar 
    14.Najafabadi, M. M. et al. Deep learning applications and challenges in big data analytics. J. Big Data 2, 1 (2015).Article 

    Google Scholar 
    15.Dieleman, S., Brakel, P. & Schrauwen, B. Audio-based music classification with a pretrained convolutional network. In ISMIR (2011).16.Lee, H., Pham, P., Largman, Y. & Ng, A. Unsupervised feature learning for audio classification using convolutional deep belief networks. In Advances in Neural Information Processing Systems22 (2009).17.Bergler, C. et al. Orca-spot: An automatic killer whale sound detection toolkit using deep learning. Sci. Rep. 9, 10997 (2019).ADS 
    Article 

    Google Scholar 
    18.Zhong, M. et al. Beluga whale acoustic signal classification using deep learning neural network models. J. Acoust. Soc. Am. 147, 1834–1841 (2020).ADS 
    Article 

    Google Scholar 
    19.Strout, J. et al. Anuran call classification with deep learning. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2662–2665 (2017).20.Salamon, J., Bello, J. P., Farnsworth, A. & Kelling, S. Fusing shallow and deep learning for bioacoustic bird species classification. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2017).21.Stowell, D., Wood, M. D., Pamuła, H., Stylianou, Y. & Glotin, H. Automatic acoustic detection of birds through deep learning: The first bird audio detection challenge. Methods Ecol. Evol. 10, 368–380. https://doi.org/10.1111/2041-210X.13103 (2019).Article 

    Google Scholar 
    22.[Dataset] Cornell Lab of Ornithology. Cornell birdcall identification. https://www.kaggle.com/c/birdsong-recognition (accessed 15 Jun 2020).23.McFee, B. et al. librosa: Audio and music signal analysis in python. In Proceedings of the 14th Python in Science Conference 8 (2015).24.Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In ICLR (2015).25.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In CVPR 770–778 (2016).26.Billerman, S. M., Keeney, B. K., Rodewald, P. G. & Schulenberg, T. S. (eds.) Birds of the World Cornell Laboratory of Ornithology, Ithaca, NY, USA, 2020). https://birdsoftheworld.org/bow/home.27.Gu, A., Dao, T., Ermon, S., Rudra, A. & Re, C. Hippo: Recurrent memory with optimal polynomial projections (2020). arXiv:2008.07669.28.Molau, S., Pitz, M., Schluter, R. & Ney, H. Computing mel-frequency cepstral coefficients on the power spectrum. In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) 1, 73–76 (2001). https://doi.org/10.1109/ICASSP.2001.940770.29.Choi, K., Fazekas, G. & Sandler, M. Automatic tagging using deep convolutional neural networks (2016). arXiv:1606.00298.30.Dieleman, S. & Schrauwen, B. End-to-end learning for music audio. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 6964–6968 (2014).31.Voelker, A., Kajic, I. & Eliasmith, C. Legendre memory units: Continuous-time representation in recurrent neural networks. In NeurIPS (2019).32.Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In CVPR 4700–4708 (2017).33.Doriana, C., Leforta, R., Bonnela, J., Zaraderb, J.-L. & Adam, O. Bi-class classification of humpback whale sound units against complex background noise with deep convolution neural network (2017). arXiv:1702.02741.34.Narasimhan, R., Fern, X. Z. & Raich, R. Simultaneous segmentation and classification of bird song using cnn. In Proc. Int. Conf. Acoust. Speech, Signal Process 146–150 (2017).35.Sankupellay, M. & Konovalov, D. Bird call recognition using deep convolutional neural network, resnet-50 (2018).36.Zhang, L., Wang, D., Bao, C., Wang, Y. & Xu, K. Large-scale whale-call classification by transfer learning on multi-scale waveforms and time-frequency features. Appl. Sci. 9, 1020 (2019).Article 

    Google Scholar 
    37.Berman, P. C., Bronstein, M. M., Wood, R. J., Gero, S. & Gruber, D. F. Deep machine learning techniques for the detection and classification of sperm whale bioacoustics. Sci. Rep. 9, 12588 (2019).ADS 
    Article 

    Google Scholar 
    38.Zhong, M. et al. Improving passive acoustic monitoring applications to the endangered cook inlet beluga whale. J. Acoust. Soc. Am. 146, 3089–3089 (2019).ADS 
    Article 

    Google Scholar 
    39.Efremova, D. B., Sankupellay, M. & Konovalov, D. A. Data-efficient classification of birdcall through convolutional neural networks transfer learning. In 2019 Digital Image Computing: Techniques and Applications (DICTA) 1–8 (2019).40.Zhong, M. et al. Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling. Appl. Acoust. 166, 107375 (2020).Article 

    Google Scholar 
    41.Thakura, A., Thapar, D., Rajan, P. & Nigam, A. Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss. J. Acoust. Soc. Am. 146, 534 (2019).ADS 
    Article 

    Google Scholar 
    42.Wang, Z., Yan, W. & Oates, T. Time series classification from scratch with deep neural networks: A strong baseline (2016). arXiv:1611.06455.43.Williams, R. J. & Zipser, D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1, 270–280 (1989).Article 

    Google Scholar 
    44.Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).ADS 
    Article 

    Google Scholar 
    45.Bengio, Y., Simard, P. & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994).CAS 
    Article 

    Google Scholar 
    46.Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).CAS 
    Article 

    Google Scholar 
    47.Cho, K., Van Merriënboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder–decoder approaches (2014). arXiv:1409.1259.48.Zeng, Y., Mao, H., Peng, D. & Yi, Z. Spectrogram based multi-task audio classification. Multimed. Tools Appl. 78, 3705–3722 (2019).Article 

    Google Scholar 
    49.Voelker, A. R. & Eliasmith, C. Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells. Neural Comput. 30, 569–609 (2018).MathSciNet 
    Article 

    Google Scholar 
    50.Xu, Y., Kong, Q., Huang, Q., Wang, W. & Plumbley, M. D. Convolutional gated recurrent neural network incorporating spatial features for audio tagging (2017). arXiv:1702.07787.51.Keren, G. & Schuller, B. Convolutional RNN: An enhanced model for extracting features from sequential data (2016). arXiv:1602.05875.52.Lai, G., Chang, W.-C., Yang, Y. & Liu, H. Modeling long- and short-term temporal patterns with deep neural networks (2017). arXiv:1703.07015.53.Shiu, Y. et al. Deep neural networks for automated detection of marine mammal species. Sci. Rep. 10, 607 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    54.Espi, M., Fujimoto, M., Kubo, Y. & Nakatani, T. Spectrogram patch based acoustic event detection and classification in speech overlapping conditions. In 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA) 117–121 (2014).55.Feng, L., Liu, S. & Yao, J. Music genre classification with paralleling recurrent convolutional neural network (2017). arXiv:1712.08370.56.Choi, K., Fazekas, G., Sandler, M. & Cho, K. Convolutional recurrent neural networks for music classification (2016). arXiv:1609.04243.57.Himawan, I., Towsey, M. & Roe, P. 3d convolution recurrent neural networks for bird sound detection. In Wood, M., Glotin, H., Stowell, D. & Stylianou, Y. (eds.) Proceedings of the 3rd Workshop on Detection and Classification of Acoustic Scenes and Events 1–4 (Detection and Classification of Acoustic Scenes and Events, 2018).58.Cakir, E., Adavanne, S., Parascandolo, G., Drossos, K. & Virtanen, T. Convolutional recurrent neural networks for bird audio detection. In 2017 25th European Signal Processing Conference (EUSIPCO) 1744–1748 (2017). More

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    The rate and fate of N2 and C fixation by marine diatom-diazotroph symbioses

    Abundances of N2 fixing symbioses in the WTNATo date, the various marine symbiotic diatoms are notoriously understudied, and hence our understanding of their abundances and distribution patterns is limited [7]. In general, these symbiotic populations are capable of forming expansive blooms, but largely co-occur at low densities in tropical and subtropical waters with a few rare reports in temperate waters [26,27,28,29, 39,40,41,42]. The Rhizosolenia-Richelia symbioses have been more commonly reported in the North Pacific gyre [26, 27, 31], and the western tropical North Atlantic (WTNA) near the Amazon and Orinoco River plumes is an area where widespread blooms of the H. hauckii-Richelia symbioses are consistently recorded [28, 29, 42,43,44,45,46,47].In the summer of 2010, bloom densities (105−106 cells L−1) of the H. hauckii-Richelia symbioses were encountered at multiple stations with mesohaline (30–35 PSU) surface salinities (Supplementary Table 1). The R. clevei-Richelia symbioses were less abundant (2–30 cells L−1). Similar densities of H. hauckii-Richelia have been reported in the WTNA during spring (April–May) and summer seasons (June–July) (28–29; 46). In fall 2011, less dense symbiotic populations (0–50 cells L−1) were observed, and the dominant symbioses was the larger cell diameter (30–50 µm) H. membranaceus associated with Richelia. Previous observations of H. membranaeus-Richelia in this region are limited and reported as total cells (i.e., 12-218 cells) and highest numbers recorded in Aug–Sept in waters near the Bahama Islands [43]. On the other hand, Rhizosolenia-Richelia are even less reported in the WTNA, and most studies by quantitative PCR assays based on the nifH gene (for nitrogenase enzyme for N2 fixation) of the symbiont (44; 46–7). Unlike qPCR which cannot resolve if the populations are symbiotic or active for N2 fixation, the densities and activity reported here represent quantitative counts and measures of activity for symbiotic Rhizosolenia.The WTNA is largely influenced by both riverine and atmospheric dust deposition (e.g., Saharan dust) [48], including the silica necessary for the host diatom frustules, and trace metals (e.g., iron) necessary for photosynthesis by both partners and the nitrogenase enzyme (for N2 fixation) of the symbiont. We observed similar hydrographic conditions (i.e., low to immeasurable concentrations of dissolved N, sufficient concentrations of dissolved inorganic P and silicates, and variable surface salinities; 22; 28–29; 40–47) as reported earlier that favor high densities of H. hauckii-Richelia blooms. Unfortunately our data is too sparse to determine if these conditions are in fact priming and favoring the observed blooms of the H.hauckii-Richelia symbioses in summer 2010, and to a lesser extent in the Fall 2011.A biometric relationship between C and N activity and host biovolumeThe diatom-Richelia symbioses are considered highly host specific [10, 11], however, the driver of the specificity between partners remains unknown. We initially hypothesized that host selectivity could be related to the N2 fixation capacity of the symbiont. Moreover, it would be expected that the larger H. membranaceus and R. clevei hosts which are ~2–2.5 and 3.5–5 times, respectively, larger in cell dimensions than the H. hauckii cells would have higher N requirements (Supplementary Table 2). In fact, recently it was reported that the filament length of Richelia is positively correlated with the diameter of their respective hosts [22]. Thus, to determine if there is also a size dependent relationship between activity and cell biovolume, the enrichment of both 15N and 13C measured by SIMS was plotted as a function of symbiotic cell biovolume.Given the long incubation times (12 h) and previous work [32] that show fixation and transfer of reduced N to the host is rapid (i.e., within 30 min), we expected most if not all of the reduced N, or enrichment of 15N, to be transferred to the host diatom during the experiment (Fig. 1). Therefore, we measured and report the enrichment for the whole symbiotic cell, rather than the enrichment in the individual partners (Supplementary Table 2; Fig. 2). The enrichment of both 13C/12C and 15N/14N was significantly higher in the larger H. membranaceus-Richelia cells (atom % 13C: 1.5628–2.0500; atom % 15N: 0.8645–1.0200) than the enrichment measured in the smaller H. hauckii-Richelia cells (atom % 13C: 1.0700–1.3078; atom % 15N: 0.3642–0.7925) (Fig. 2) (13C, Mann–Whitney p = 0.009; 15N, Mann–Whitney p 50 symbiotic cells in a chain) were reported at station 2 with fully intact symbiotic Richelia filaments (2–3 vegetative cells and terminal heterocyst), and at station 25 chains were short (1–2 symbiotic cells) and associated with short Richelia filaments (only terminal heterocyst). Moreover, the symbiotic H. hauckii hosts possessed poor chloroplast auto-fluorescence at station 25 [46]. Given that the cells selected for NanoSIMS were largely single cells, rather than chains, we suspect that these cells were in a less than optimal cell state, which was also reflected in the low 13C/12C enrichment ratios and low estimated C-based growth rates (0.30–57 div d−1). These are particularly reduced compared to the growth rates recently reported for enrichment cultures of H. hauckii-Richelia (0.74–93 div d−1§) (Supplementary Table 2) [33].In 2011, higher cellular N2 fixation rates (15.4–27.2 fmols N cell−1 h−1) were measured for the large cell diameter H. membranaceus-Richelia, symbioses. Despite high rates of fixation, cell abundances were low (4–19 cells L−1), and resulted in a low overall contribution of the symbiotic diatoms to the whole water N2 ( >1%) and C-fixation ( >0.01%). The estimated C-based growth rates for H. membranaceus were high (1.9–3.5 div d−1), whereas estimated N-based growth rates (0.3–4 div d−1) were lower than previously published (33; 52–53). Hence the populations in 2011 were likely in a pre-bloom condition given the low cell densities.Estimating symbiotically derived reduced N to surface oceanTo date, determining the fate of the newly fixed N from these highly active but fragile symbiotic populations has been difficult. Thus, we attempted to estimate the excess N fixed and potentially available for release to the surround by using the numerous single cell-specific rates of N2 fixation determined by SIMS on the Hemiaulus spp.-Richelia symbioses (Supplementary Materials). Because the populations form chains during blooms and additionally sink, we calculated the size-dependent sinking rates for both single cells and chains ( >50 cells). Initially we hypothesized that sinking rates of the symbiotic associations would be more rapid than the N excretion rates, such that most newly fixed N would contribute less to the upper water column (sunlit).The sinking velocities were plotted (Fig. 5) as a function of cell radius at a range (min, max) of densities and included two different form resistances (∅ = 0.3 and 1.5). As expected, the combination of form resistance and density has a large impact on the sinking velocity. For example, a H. hauckii cell of similar radius (10 μm) and density (3300 kg m−3) but higher form resistance (0.3 vs. 1.5) sinks twice as fast at the lower form resistance (Fig. 5). This points to chain formation (e.g., increased form resistance) as a potential ecological adaptation to reduce sinking rates. Recently, colony formation was identified as an important phenotypic trait that could be traced back ancestrally amongst both free-living and symbiotic diatoms that presumably functions for maintaining buoyancy and enhancing light capture [22].Fig. 5: The influence of cell characteristics on estimated sinking velocity for symbiotic Hemiaulus spp.The range of diatom sinking speed predicted using the modified Stokes approximation for diatoms [74] and accounting for the symbioses (cylinders) having varying cell size characteristics (form resistance by altering chain length, density; Supplementary Table 4). Note that form resistance increases with chain length and that the longest chains would have sinking speeds less than 10 m d−1.Full size imageThe concentration of fixed N surrounding a H. hauckii and H. membranaceus cell were modeled (Supplementary Materials; Supplementary Table 4; Fig. 6). First, the cellular N requirement (QN, mol N cell−1) for a cell of known volume, V, as per the allometric formulation of Menden-Deuer and Lessard [71] is calculated by the following.$${{{{{{{mathrm{Q}}}}}}}}_{{{{{{{mathrm{N}}}}}}}} = (10^{ – 12}/12) times 0.76 ;times, {{{{{{{mathrm{V}}}}}}}}^{^{0.189}}$$
    (1)
    Fig. 6: The simplified case of diffusive nitrogen (N) exudate plumes for non-motile symbioses.The concentration of dissolved N (nmol L−1) is presented at of varying cell sizes (3 µm and 30 µm) for H. hauckii-Richelia (A and B, respectively) and H. membranaceus-Richelia (C and D, respectively) growing at specific growth rates of 0.4 d−1 (dashed red lines) or 0.68 d−1 (solid black lines). Exudation follows the same principle as diffusive uptake as per Kiorboe [72] in the absence of turbulence.Full size imageVolume calculations assume a cylindrical shape; whereas exudation assumes an equivalent spherical volume. Then, using published growth rates of 0.4 d−1 and 0.68 d−1 for the symbioses [52, 53], N uptake rate (VN) necessary to sustain the QN was determined. N loss was assumed to be a constant fraction (f) of the VN; this fraction was assumed to be 7.5% and 11% for H. hauckii and H. membranaceus, respectively, or the estimated excess N which was fixed given the assumed growth rate [31]. The excretion rate (EN) of the individual cells was then calculated as$${{{{{{{mathrm{E}}}}}}}}_{{{{{{{mathrm{N}}}}}}}} = {{{{{{{mathrm{fQ}}}}}}}}_{{{{{{{mathrm{N}}}}}}}}$$
    (2)
    The concentration of fixed N surrounding the cell (Cr) was iteratively calculated by the following:$${{{{{{{mathrm{C}}}}}}}}_{{{{{{{mathrm{r}}}}}}}} = {{{{{{{mathrm{E}}}}}}}}_{{{{{{{mathrm{N}}}}}}}}/(4pi * {{{{{mathrm{D}}}}}}* {{{{{mathrm{r}}}}}}_{{{{{mathrm{{x}}}}}}}) + {{{{{{{mathrm{C}}}}}}}}_{{{{{{{mathrm{i}}}}}}}}$$
    (3)
    The concentric radius (rx) as per Kiørboe [72] uses a diffusivity of N assumed to be 1.860 × 10−5 cm2 sec−1 and the background concentration of N (Ci) is assumed to be negligible. Figure 5 presents the results for the two symbioses: H. membranaceus and H. hauckii at the two growth rates and as chains or singlets. Mean sinking rates for cells with a high form resistance (e.g., chains) are More