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Ina, Japan

News Article
Site: http://phys.org/physics-news/

When electrons pass through a material they encounter various degrees of resistance, causing them to lose energy along their journey.  In the 1950s physicist Philip Anderson, predicted that in some disordered materials (such as a semiconductors) electrons—-or more specifically the electrons viewed as a series of quantum waves—-could get trapped.  They become immobilized not by losing all their energy but by an interference effect by which  the waves become bottled up in a certain region. This assertion, later demonstrated in experiments, is at odds with conventional thermodynamics.  Electrons, at one temperature (in effect) entering a material at a different energy, ought to "thermalize," that is, come to a common temperature.  But localization seems to sidestep this: the electrons waves remain intact but segregated.  They don't come to the temperature of their surroundings. Many-body localization (MBL) has become a hot topic in physics.  In 2006 only three journal articles mentioned MBL; in 2015 the number was 190.  In November 2015 the Kavli Institute for Theoretical Physics held a special meeting devoted to the subject. The term ergodic dates back to the nineteenth century and was coined by Ludwig Boltzmann to describe statistically how a system of particles evolves over time. Throw a thousand identical dice and record the numerical results.  Then throw a single similar die a thousand times.  The average showing should be very similar.  This is an example of an ergodic system.  One hallmark is that space and time averages of the system should be similar.  The average die values for the "dice system" taken singly over a long time or with multiple dice at one instant. Open the stopper of a perfume bottle in a closed room and come back after a long time.  There will be an equal likelihood of a perfume molecule being in all the parts of the room.  This is another ergodic example.  A more technical way of saying this is that the total description of the ensemble of molecules explores all possible configurations of the molecules.  "Anything that can happen will happen."  One possible state of the system includes the chance that all the molecules will return to the bottle whence than had come.  But since there are trillions of other configurations where this does not happen in practice our observation is of molecules all around the room.  At the end we have no sense, sampling the molecules, that they were once all in the bottle.  The system no longer remembers its origin. What about non-ergodic systems?  Consider one person sitting in a restaurant selecting from a menu of items.  She visits the restaurant 100 times.  Compare her choices to those of one hundred people at one time ordering menu items.  Here the average statistics for ordered items will be very different?  Why?  Because humans are more choosey than dice. In experiments conducted at the Max Planck Institute in Garching, Germany and at the Joint Quantum Institute at the University of Maryland in the U.S., confined atoms displayed localization and behavior that was non-ergodic.  In the Max Planck work, neutral atoms are stored in an optical lattice; in the JQI setup, a string of ions is stored.  Instead of electrons moving through a solid material, the atoms, each with its own characteristic spin orientation, reside in a laser-driven crate environment.   Here the disorder (imposed upon the confining laser beams) imposes localization   In the German experiment, particles (the atoms) are localized. In the U.S. experiment, it is the spins of ions that are localized. To be more specific about the JQI experiment: special modulated laser beams  introduce disorder into the system of ions.  Instead of the spins all interacting witheach other, thereby losing  their original collective spin configuration, the disorder has the effect of localizing the spins in their abstract spin "space."  Without the disorder localization does not occur.  When the disorder climbs above a critical value, localization does occur; the atoms do not mix up their spins; they do not "thermalize.  "They are stuck near to their initial spin configuration," says Jacob Smith, one of the JQI experimenters.  The atomic spins retain a sense of their origin.  They are behaving non-ergodically. So, do localization and non-ergodicity go together?  Not necessarily says a new report by four JQI theorists published in Physical Review Letters. Xiaopeng Li, the lead author on the new theory paper, commented on this bizarre behavior where particles could be de-localized (they keep moving; they are not confined) and yet be non-ergodic in nature—-which is say that they do not thermalize.  "Our theory points to a possible physical picture that some particles are inert but others are active. An analogue for the case of dice would be if even numbers were equally likely but odd ones were forbidden. This exotic phase of matter provides one scenario for the localization transition of a quantum system." And since thermalization is one of the leading causes of quantum decoherence, exploiting non-ergodic systems—-whether the constituent particles were localized or extended—-might help in the storage of quantum information.   Non-ergodic systems might not be implemented in the form of conventional solid matter, but might be possible in the form of trapped atoms, as the experiments mentioned above indicated. Sankar das Sarma, the leader of team of JQI theorists working on this problem, describes non-ergodic in terms of temperature.  "We take it for granted that all systems left to themselves attain a temperature; that is, they achieve thermodynamic equilibrium.  But is this always true?  In the simplest term, ergodicity assures (almost always) the achievement of a temperature.  Non-ergodic systems are not in thermal equilibrium—-ever!—-and cannot be characterized by a temperature.  Isolated localized systems are always non-ergodic since there is no way to transport energy from one point to another to achieve equilibrium." That a body of particles could be un-localized and also non-ergodic at the same time came as a surprise to the theorists, who modeled the interactions among the particles using extensive computer simulations.  "We have to be cautious," said das Sarma. "I believe our results are correct for what we do, but whether it applies in the thermodynamic limit of a macroscopic system is still an open question of great interest.  But it might contribute to the effort to fight against intrinsic decoherence.  It could help create quantum insulating systems—-heat insulators." Explore further: What happens when ultracold atomic spins are trapped in an optical lattice structure More information: Many-Body Localization and Quantum Nonergodicity in a Model with a Single-Particle Mobility Edge Phys. Rev. Lett. 115, 186601 – Published 28 October 2015. dx.doi.org/10.1103/PhysRevLett.115.186601


News Article
Site: http://www.nature.com/nature/current_issue/

No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. The following antibodies were used in this study: mouse-anti-actin (clone ac-15, Sigma-Aldrich, western blotting (WB): 1:10,000), mouse-anti-AP-1γ (γ-adaptin, clone 100/3, Sigma-Aldrich, immunofluorescence (IF): 1:100), mouse-anti-AP-2α (α-adaptin, clone AP-6, hybridoma cell line, IF: 1:100), rabbit-anti-APPL1 (Cell Signalling, IF: 1:100), mouse-anti-B10 (IGBMC, WB: 1:2,000), mouse-anti-β1-integrin (clone LM534, Millipore, IF: 1:375), mouse-anti-β1-tubulin (clone B5-1-2, Sigma-Aldrich, WB: 1:500), rabbit-anti-clathrin heavy chain (Abcam, IF: 1:500), mouse-anti-clathrin heavy chain (clone TD1, hybridoma cell line, WB: 1:500), rabbit-anti-EEA1 (Cell Signalling, IF: 1:100), mouse-anti-EGFR (clone R-1, Santa Cruz, IF: 1:100), mouse-anti-Exo70 (Millipore, WB: 1:500, IF: 1:100), rabbit-anti-Gadkin (ref. 25, WB: 1:1,000), mouse-anti-GM130 (BD Transduction, IF: 1:100), rabbit-anti-GFP (Abcam, WB: 1:10,000, IF: 1:500), mouse-anti-HA (clone HA.11, Covance, IF: 1:400), rabbit-anti-HA (Cayman Chemical, IF: 1:100), rabbit-anti-HA (clone Y-11, Santa Cruz, WB: 1:500), mouse-anti-HA-Alexa Fluor 488 (clone HA.11, Covance, IF: 1:100), mouse-anti-LAMP1 (BD Pharmingen, IF: 1:200), mouse-anti-LC3B (clone 4E12, MBL International, IF: 1:100), rabbit-anti-MTM1 (raised against amino acid 19-33 and amino acid 502-516 of human MTM1, WB: 1:250), mouse-anti-PI(4)P (catalogue: Z-P004, Echelon Biosciences, IF: 1:63), mouse-anti-PI(4,5)P (catalogue: Z-A045, Echelon Biosciences, IF: 1:200), mouse-anti-PI(3,4)P (catalogue: Z-P034b, Echelon Biosciences, IF: 1:150), rabbit-anti-PI4K2α (ref. 26, WB: 1:2,000), sheep-anti-PIKfyve (Tocris Biosciences, WB: 1:1,000), mouse-anti-Rab5 (BD Transduction, IF: 100), rabbit-anti-Rab7 (clone D95F2, Cell Signalling, IF: 1:50), rabbit-anti-Rab11a (Life Technologies, WB: 1:500), rabbit-anti-Sec3 (Proteintech Group, WB: 1:500), mouse-anti-Sec6 (Stressgen, WB: 1:500), mouse-anti-Sec8 (BD Transduction, WB: 1:500), mouse-anti-SNX4 (Sigma-Aldrich, WB: 1:500), mouse-anti-SNX17 (Proteintech Group, WB: 1:1,000), sheep-anti-TGN46 (Serotec, IF: 1:200), mouse-anti-TfR (clone H68.4, Life Technologies, IF/flow cytometry: 1:200), rabbit-anti-TfR (Sigma-Aldrich, IF: 1:100), rabbit-anti-Vps26 (Abcam, IF: 1:100), rabbit-anti-Vps34 (clone D9A5, Cell Signalling, WB: 1:1,000). All siRNAs used in this study were 21-, 23-, or 27-base oligonucleotides including 3′-dTdT overhangs. For silencing, the following siRNAs were used targeting the human isoform: Exo70 5′-GGTTAAAGGTGACTGATTA-3′, MTM1 5′-GATGCAAGACCCAGCGTAA-3′, MTMR1 5′-GAGATAGTGTGCAAGGATA-3′, MTMR2 5′-GGACATCGATTTCAACTAA-3′, MTMR4 5′-CAGCATAGGTTACGGCAAA-3′, MTMR7 5′-TGCAAGAACTTTCAGATAA-3′, PI4K2α 5′-GGATCATTGCTGTCTTCAA-3′, Rab11a 5′-AAGAGCGATATCGAGCTATAA-3′, Sec3 5′-CCTGTTGGATATGGGAAACAT-3′, Sec6 5′-CTGGAGGCAGAGCATCAACAC-3′, SNX4 5′-TGGTCAGAGTGTCCTAACA-3′, SNX17 5′-CTGGCTTTTGAATACCTCA-3′, and Vps34 5′-CCCATGAGATGTACTTGAACGTAAT-3′. For silencing Kif16b and PIKfyve, a pool of four siRNAs was obtained from Dharmacon (Thermo Scientific). The scrambled control siRNA used throughout this study corresponded to the scrambled γ1 adaptin sequence 5′-AAATCGGATATCGGAATAG-3′. Complementary DNA encoding full-length human MTM1 and MTMR2 was provided by G. Di Paolo and inserted into a pcDNA3.1(+)-based haemagglutinin (HA)-, mCherry-, or eGFP-expression vector with tags at the amino (N) terminus of MTM1 and MTMR2. siRNA-resistant MTM1 constructs were created by introducing four silent mutations: 5′-gatgc ag cc ag gtaa-3′. P205L, R241L, Y397C, and C375S mutants of MTM1 were created by mutation of the respective amino acid of human MTM1. N-terminally tagged human MTMR7 and mouse MTMR1 were in a pcDNA3.1(+)-based vector backbone. N-terminally tagged GFP–MTMR4 was a gift from M. Clague, N-terminally tagged GFP–Rab4A and GFP–Rab11A were a gift from P. van Sluis, and GFP–Rab14 was provided by T. Proikas-Cezanne. N-terminally tagged GFP–Rab5A GFP–Rab8A and GFP–Rab35 were in the pEGFP-C vector backbone (Clonetech) and Rab5A in a pcDNA3.1(+) vector backbone. Q79L mutant of Rab5A was created by mutation of the respective amino acid of human Rab5A. Full-length human SNX1, SNX3, SNX4, SNX8, SNX15, SNX17, and SNX27 were inserted in a pcDNA3.1(+)-based expression vector to express N-terminally tagged eGFP–fusion proteins except for carboxy (C)-terminally tagged SNX17–eGFP. N-terminally tagged eGFP–2xFYVE-Hrs, eGFP–2xPH-PLCδ and eGFP–2xPH-FAPP1 were in a pcDNA3.1(+)-based expression vector. The mRFP-2xPH-TAPP1 was a gift from T. Takenawa and the eGFP–2xPH domain of Bruton’s tyrosine kinase was a gift from M. Wymann. N-terminally tagged eGFP–mPI4K2α was in a pcDNA3.1(+)-based expression vector. N-terminally tagged HA-mPI4K2α was in the pcDNA5/FRT/TO expression vector (Life Technologies). D308A mutant of PI4K2α was created by mutation of the respective amino acid of mouse PI4K2α. To create N-terminally tagged GST-MTM1 and -Sec6, full-length human MTM1 and Sec6 were cloned in the Gateway pENTRA1 entry vector and by recombination transferred into the Gateway pGEX4T3 vector (Life Technologies). N-terminally tagged mouse PI4K2α was in the pGEX4T1 vector backbone. The B10-tag was engineered in-house by inserting the B10 epitope of the human oestrogen receptor in the pSG5 vector backbone (Stratagene) and used to create N-terminally tagged B10-MTM1 and B10-Sec6 (pSG5hERB10 empty vector). HeLa and COS-1 cells were from ATCC and not used beyond passage 30 from original derivation from ATCC without further authentication. Hek293 cells stably expressing HA-tagged mPI4K2α were generated using the FlpIn system developed by Life Technologies according to the manufacturer’s protocol. The fibroblast cell line H31 from patients with XLCNM has a genomic deletion of the entire MTM1 (refs 27, 28); the fibroblast cell line G92-628 from a patient with XLCNM, referred to as XLCNM patient 2, has a stop mutation in MTM1 at amino acid 37 (refs 27, 28). HDFa cells (human dermal fibroblast from adult healthy individuals) were obtained from Life Technologies. H31, G92-628, and HDFa cells were cultured in MEM (Life Technologies) containing 15% FCS and not used beyond passage 20. HeLa-M Clone 1 (C1) cells were cultured in DMEM (4.5 g l−1 glucose, Life Technologies) containing 10% FCS and 1.66 μg ml−1 puromycin and not used beyond passage 10 (ref. 29). All cell lines were routinely tested for mycoplasma contaminations on a monthly basis. HeLa cells were transfected with siRNA using Oligofectamin (Life Technologies) according to the manufacturer’s protocol. To achieve optimal knockdown efficiency, two rounds of silencing were performed. Cells were transfected on day 1, expanded on day 2, transfected for a second time on day 3, seeded for the experiment on day 4, and the experiment was performed on day 5. For transient overexpression of proteins in knockdown cells, plasmids were transfected on day 4 using Lipofectamin 2000 (Life Technologies) according to the manufacturer’s protocol. In case of co-transfection of two plasmids, JetPrime (Polyplus) was used according to the manufacturer’s protocol. For transient overexpression of proteins in untreated cells, plasmids were transfected 24 h before analysis using Lipofectamin 2000 (Life Technologies). For knockdown of transiently overexpressed proteins, cells were simultaneously transfected with plasmids and siRNA using Lipofectamin 2000 according to the manufacturer’s protocol; expression was allowed overnight and cells analysed the next day. HDFa, H31, and COS-1 cells were transfected using Lipofectamin 3000 (Life Technologies) according to the manufacturer’s protocol; expression was allowed overnight and cells analysed the next day. Cells seeded on coverslips coated with Matrigel (BD Biosciences) were serum-starved for 2 h and used for either Tf uptake or surface labelling. For quantitative Tf uptake, cells were treated with 25 μg ml−1 Tf-Alexa647 (Life Technologies) for 10 min at 37 °C. After being washed twice with ice-cold phosphate-buffered saline (PBS), cells were acid washed at pH 5.3 (0.2 M Na-acetate, 0.2 M NaCl) for 1 min on ice to remove surface-bound Tf, followed by washing twice with ice-cold PBS and fixation with 4% paraformaldehyde (PFA) for 45 min at room temperature (23–25 °C). For all immunocytochemistry stainings except MTM1 or Rab5 Q79L-expressing cells, inhibitor treatments (Wortmannin or Vps34-IN1), and staining of β1-integrin, cells were treated with 25 μg ml−1 Tf-Alexa647 (Life Technologies) for 30 min at 37 °C to allow Tf uptake to reach saturation. Cells were washed twice with PBS, followed by immunocytochemistry staining as described in Immunocytochemistry and spinning disc confocal imaging. For TfR surface labelling, cells were incubated with 25 μg ml−1 Tf-Alexa647 for 45 min at 4 °C to block endocytosis, washed three times with ice cold PBS, and fixed with 4% PFA for 45 min at room temperature. Tf uptake and surface labelling, and EGFR and LC3B labelling, were analysed using a Nikon Eclipse Ti microscope (eGFP filter set: F36-526; TexasRed filter set: F36-504; Cy5 filter set: F46-009; DAPI filter set: F46-000), equipped with a ×40 oil-immersion objective (Nikon), a sCMOS camera (Neo,Andor), and a 200 W mercury lamp (Lumen 200, Prior), operated by open-source ImageJ-based micromanager software and quantified using open-source ImageJ software. Levels of eGFR, LC3B, and internalized or surface-bound Tf were normalized to the cell area. The ratio of internalized Tf to surface-bound Tf was used to distinguish between uptake and recycling defects. Owing to small differences in TfR level in HDFa and H31 cells, surface-bound Tf was normalized to TfR level. TfR levels in HDFa and H31 cells were analysed by TfR antibody staining (see Immunocytochemistry and spinning disc confocal imaging) and analysed using a Nikon Eclipse Ti microscope as described above. Cultured cells seeded on Matrigel-coated coverslips were fixed for 10 min with 4% PFA, washed twice with PBS, permeabilized in blocking solution (10% goat serum, 20 mM Na H PO pH 7.4, 0.3% Triton X-100, 100 mM sodium chloride) for 30 min, and incubated with primary antibodies diluted in blocking solution for 1 h. After three washes with washing solution (20 mM Na H PO pH 7.4, 0.3% Triton X-100, 100 mM NaCl), secondary antibodies diluted in blocking solution were incubated for 1 h, followed by three washes in washing solution. For transient overexpression of eGFP–MTM1, cells were washed twice with ice-cold PBS, incubated with PEM (80 mM Pipes pH 6.8, 5 mM EGTA, 1 mM MgCl ) containing 0.05% saponin for 5 min at 4 °C, followed by a brief wash with ice-cold PEM. After fixation with 3% PFA for 15 min at 4 °C, cells were washed three times with PBS at room temperature, incubated with 50 mM NH Cl for 10 min and washed again twice with PBS. Immunocytochemistry was done as described above with the exception that Triton X-100 was exchanged for 0.05% saponin in all buffers. PI(3)P and PI(4)P stainings were performed as previously described30, although at a reduced concentration of eGFP–2xFYVE of 0.25 μg ml−1. If indicated, PI(3)P was labelled using a GST-tagged Phox domain of p40 chemically conjugated to Alexa Fluor 488, which was a gift from I. Ganley. For wortmannin (Sigma-Aldrich) treatment, cells were incubated with 2 μm wortmannin (dissolved in dry dimethylsulfoxide (DMSO)) or DMSO, diluted in serum-free medium, for 30 min at 37 °C, and subsequent PI(3)P staining was performed as described above. PI(3,4)P and PI(4,5)P stainings were performed as previously described31. For all LC3 immunocytochemistry stainings, fresh serum-containing medium was added 2 h before fixation. In case of bafilomycin A1 (BafA1, Sigma-Aldrich) treatment cells were washed three times with HBSS and incubated with 100 nM BafA1 (dissolved in dry DMSO) diluted in HBSS for 3 h. Control cells were incubated with DMSO diluted in serum-containing medium. After fixation with 4% PFA for 30 min, cells were washed three times with PBS, permeabilized with 200 μg ml−1 digitonin diluted in PBS for 10 min, followed by three washes with PBS. Cells were incubated with the primary antibody diluted in PBS for 1 h, followed by three washes with PBS. Secondary antibodies diluted in PBS were incubated for 1 h, followed by three washes in PBS. Protein and lipid immunocytochemistry stainings were routinely analysed and quantified using a spinning disc confocal microscope (Ultraview ERS, Perkin Elmer) with Volocity imaging software (Improvision, Perkin Elmer). For all quantifications, protein and lipid stainings were normalized to cell area. For quantification of TfR localization, cells with either perinuclear or peripheral TfR localization were counted. Peripheral TfR localization was defined as cells with either TfR dispersion or TfR accumulations at the cell periphery as shown in example images (Fig. 2c), and the normalized fraction of cells with perinuclear TfR localization was quantified. The amount of co-localization between two channels was quantified using thresholded Pearson’s correlation coefficients. To quantify the amount of co-localization at peripheral sites, thresholded Pearson’s coefficients were calculated in three randomly chosen 100 pixel × 100 pixel squares in the cell periphery. For averaged line scans, line profiles were calculated as the mean fluorescence intensity averaged over 100 pixels. Maximum intensity projections were calculated from z-stacks with 200 nm spacing between slices covering the whole cell. For statistical analysis, see Statistical analysis of immunocytochemistry and live-cell TIRF experiments. Cells seeded on Matrigel-coated coverslips were serum-starved for 30 min and treated with 1 μg ml−1 cholera toxin subunit B (Ctx) CF568 (Biotium) for 45 min at 37 °C, followed by 30 min chase in starvation medium. After being washed once with PBS, cells were fixed with 4% PFA for 30 min, washed twice with PBS, permeabilized in blocking solution (10% goat serum, 20 mM Na H PO pH 7.4, 0.3% Triton X-100, 100 mM sodium chloride) for 15 min and incubated with primary antibody diluted in blocking solution for 30 min. After three brief washes with PBS, secondary antibody diluted in blocking solution was incubated for 30 min, followed by three brief washes in PBS. C1 cells seeded on Matrigel-coated coverslips were washed once with PBS and treated with 1 μM D/D solubilizer (Clontech) diluted in HBSS to initiate secretion of the reporter construct. To halt secretion at the indicated time points, cells were placed on ice, washed twice with ice-cold PBS containing 10 mM MgCl and fixed with 4% PFA for 20 min at room temperature, followed by washing twice with PBS. Secretion of the GFP-tagged reporter construct was analysed using a Nikon Eclipse Ti microscope (see Transferrin uptake and surface labelling). Fifteen minutes after initiating secretion the reporter is localized at the Golgi complex. To calculate the secretion from the Golgi, all time points were normalized to 15 min. TIRF microscopy was performed using a Nikon Eclipse Ti microscope, equipped with an incubation chamber (37 °C), a ×60 TIRF objective (oil-immersion, Nikon), a sCMOS camera (Neo, Andor), a 200 W mercury lamp (Lumen 200, Prior), a triple-colour TIRF setup (laser lines: 488 nm, 568 nm, 647 nm), and operated by open-source ImageJ-based micromanager software. Cells seeded on Matrigel-coated coverslips were treated with 50 μg ml−1 Tf-Alexa647 (Life Technologies), diluted in serum-free medium for 30 min at 37 °C and 5% CO . For analysis of transferrin exocytosis, time-lapse movies of 15–30 s with a frame rate of 5 Hz were recorded. For all time-lapse movies, the 488 nm channel was acquired before the 647 nm channel, except for Extended Data Fig. 9a, g. Fusion events with the plasma membrane were defined by their characteristic time course (appearance, broadening/spreading of the fluorescence signal, and disappearance), counted and normalized to cell area. Representative kymographs were chosen over 30 s along a line of 400 pixels in length. For statistical analysis, see Statistical analysis of immunocytochemistry and live-cell TIRF experiments. Cell-permeable acetoxy methylester (AM)-protected phosphatidylinositol derivatives were synthesized according to published procedures17. For treatment of cells, PI(3)P/AM and PI(4)P/AM were dissolved in dry DMSO and mixed with an equal volume of 10% pluronic F127 in DMSO (Sigma-Aldrich). PIP/AMs were diluted in serum-free medium to a final concentration of 100 μM. For immunocytochemistry stainings, cells seeded on Matrigel-coated coverslips were treated with DMSO + pluronic F127 (control) or PIP/AMs for 30 min at 37 °C and then processed as described above. For TfR immunocytochemistry stainings, cells were stimulated with 25 μg ml−1 Tf-Alexa647 during the PIP/AM treatment. For live-cell TIRF imaging, cells seeded on Matrigel-coated coverslips were treated with 25 μg ml−1 Tf-Alexa647 and DMSO + pluronic F127 (control) or PIP/AMs for 30 min at 37 °C, directly followed by live-cell imaging. For the analysis of β1-integrin accumulations, H31 or HDFa cells were seeded on Matrigel-coated coverslips and treated with DMSO or Vps34-IN1 at a concentration of 0.01–1 μM dissolved in dry DMSO and diluted in serum-containing medium for 48 h, adding freshly diluted Vps34-IN1 or DMSO after 24 h. β1-integrin staining was performed as described in Immunocytochemistry and spinning disc confocal imaging. For live-cell TIRF imaging (see Fig. 3d), cells seeded on Matrigel-coated coverslips were treated with 50 μg ml−1 Tf-Alexa647 and DMSO or 1 μM VPS34-IN1 for 60 min at 37 °C, directly followed by live-cell imaging in the presence of DMSO or VPS34-IN1, respectively. Human EGF was purchased from Peprotech and 125I-labelled EGF from Perkin Elmer. 125I-labelled EGF degradation was performed as described previously32. Human holo-transferrin was purchased from Sigma-Aldrich and 125I-labelled Tf from Perkin Elmer. HeLa cells seeded in 24-well plates were starved for 1 h in serum-free medium containing 0.1% BSA and 20 mM HEPES pH 7.4. Cells were stimulated with 1 μg ml−1 125I-labelled Tf in starvation medium at 37 °C for the indicated time points and washed twice on ice with PBS. Surface-bound 125I-labelled Tf was removed by an acid wash with 0.2 M acetic acid, 0.5 M NaCl for 5 min on ice, collected, and radioactivity was measured using a scintillation counter (HIDEX 300SL). Cells were dried at room temperature for 5 min, lysed with 1 M NaOH for 60 min and the radioactivity of the lysate measured (corresponding to internalized Tf). Non-specific binding was measured for each time-point in the presence of a 300-fold excess of cold Tf and was subtracted from all values. The ratio of internalized to surface-bound Tf was plotted over time and Michaelis constant (K ) values were calculated using Prism software (GraphPad). On ice, cells were washed once with ice-cold PBS and detached from the culture dish by incubating for 5 min on ice with 0.1% PronaseE (Sigma-Aldrich), 0.5 mM EDTA solution in PBS. Cells were resuspended in PBS, pelleted at 300 g for 5 min at 4 °C and fixed in 4% PFA, 4% sucrose in PBS for 20 min at room temperature. Ten times excess volume of blocking solution (0.05% Saponin, 0.01% BSA in PBS) was added, then cells were pelleted and resuspended in blocking solution. After 15 min primary antibody (mouse-anti-TfR) was added and incubated for an additional 1 h. After washing once with blocking solution, cells were incubated for 1 h with secondary antibody diluted in blocking solution, followed by washing once with 0.2% BSA in PBS. Cells were resuspended in 0.2% BSA in PBS and analysed by flow cytometry using a BD LSRFortessa. Cells from a 100% confluent 10 cm cell culture dish were harvested in homogenization buffer (20 mM HEPES pH 7.4, 100 mM KCl, 2 mM MgCl , 1 mM PMSF, 0.1% protease inhibitor cocktail) and homogenized using a European Molecular Biology Laboratory cell cracker (HGM; inner diameter 8.020 mm, ball diameter 8.004 mm, with 12 strokes), followed by three freeze–thaw cycles in liquid nitrogen. Total cell lysate was collected after centrifugation at 1,000 g for 5 min at 4 °C. To obtain the cytosolic fraction, total cell lysate was centrifuged at 100,000 g for 30 min at 4 °C, the supernatant was collected, and protein concentration and volume determined. The membrane pellet was washed once in homogenization buffer and collected in a volume corresponding to the volume of the cytosol fraction. Equal volumes of total cell lysate (minimal concentration 0.125 μg μl−1, corresponding to minimal loading of 5 μg), membrane pellet, and cytosol fraction were loaded onto a 10% acrylamide gel for SDS–polyacrylamide gel electrophoresis (SDS–PAGE) followed by immunoblotting. Western blot development was done using a LI-COR Odyssey Fc imager, and western blot bands were quantified using Image Studio Lite Version 4.0 software (LI-COR). For Exo70, Sec3, Sec8, and MTM1, protein levels in the total cell lysate were normalized to actin, whereas protein levels in the membrane fraction were normalized to gadkin. The ratio of membrane to total cell lysate was used to quantify the membrane fraction of Exo70, Sec3, Sec8, and MTM1. All knockdown conditions were normalized to membrane fractions of the respective protein in scrambled siRNA-treated controls. Data are presented as mean values ± s.e.m. from five independent experiments (n). Statistical testing was performed using a one-sample t-test. Expression of HA-tagged PI4K2α in stably transfected Hek FlpIn cells was induced overnight by addition of doxycylin. As a control, untransfected Hek FlpIn cells were used. Cells were harvested in lysis buffer (20 mM HEPES pH 7.4, 100 mM KCl, 2 mM MgCl , 1 mM PMSF, 0.1% protease inhibitor cocktail, 1% Triton X-100) and incubated on ice for 30 min, followed by centrifugation at 43,500 g for 20 min at 4 °C. The supernatant was centrifuged again at 265,000 g for 15 min at 4 °C. HA-matrix beads (Covance, mouse-anti-HA.11) were used to immunoprecipitate HA-tagged PI4K2α. Protein (3–5 mg ml−1) was loaded on 30 μl 1:1 washed HA-matrix beads slurry and incubated for 1 h at 4 °C on a rotating wheel. Beads were pelleted, washed twice with lysis buffer, followed by two washes with homogenization buffer, and bound protein was eluted in 60 μl 1× Laemmli sample buffer. Eluates were loaded onto a 10% acrylamide gel for SDS–PAGE followed by immunoblotting. Pulldown assays using GST-tagged PI4K2α as a bait were performed as described previously26. For pulldown assays using GST-tagged MTM1 and Sec6, GST fusion proteins were expressed in Escherichia coli BL21 pRARE strain. For negative control, the empty pGEX4T3 vector (GST alone) was used. The induction of expression was performed with 1 mM IPTG at 16 °C for 14 h. Bacteria were lysed on ice by sonication in lysis buffer (50 mM Tris HCl pH7, 200 mM NaCl, 1 mM EDTA, 1 mM DTT, complete protease inhibitor cocktail (PIC, Roche)) supplemented with 1 mg ml−1 lysozyme, 1 mM PMSF, 0.1% sarcosyl, 0.5% Triton X-100, and then centrifuged at 15,000 g for 30 min. GST-tagged proteins were purified from bacterial lysates by incubation with Glutathione Sepharose 4B beads (GE Healthcare) for 1 h followed by extensive washing with lysis buffer plus 1 mM PMSF. In parallel, COS-1 cells expressing B10-MTM1, B10-SEC6, HA-PI4K2α, or corresponding controls were lysed with a buffer containing 20 mM HEPES-KOH pH 7.5, 200 mM NaCl, 0.5% Triton X-100, 10% glycerol, 4 mM DTT, 1 mM EDTA, and PIC by passage through 25-gauge needles. Insoluble material was removed by centrifugation at 11,000 g for 10 min. Twenty micrograms of the purified GST fusion proteins coupled to glutathione beads were then incubated with 350 μg of COS-1 cell extracts. After washing beads three times with a buffer containing 20 mM HEPES-KOH, 200 mM NaCl, 1 mM DTT, and PIC (pH 7.3), 10 μg of GST beads were analysed by electrophoresis on a 10% SDS–polyacrylamide gel. Bound B10-tagged MTM1 and Sec6 or HA-tagged PI4K2α were detected with a B10-specific antibody or a PI4K2α-specific antibody, respectively. The entire procedure was performed at 4 °C, unless specified. Liposome flotation assays were performed in Hek293 cells as previously described31. Scrambled or MTM1 siRNA transfection was performed as described in HeLa cells that were grown in 6 cm plastic dishes. Cells were serum-starved for 2 h and treated with 25 μg ml−1 transferrin-HRP (Fitzgerald) for 30 min at 37 °C. Cells were fixed with 2% glutaraldehyde in PBS. After rinsing in fresh PBS, cells were mechanically detached by scratching, pelleted, and embedded into gelatine. After osmification with aqueous 1% osmium tetroxide, samples were stained en bloc with 1% aqueous uranil acetate and embedded in epoxy resin. Sections were viewed with Zeiss 910 transmission electron microscope and micrographs were taken along the cell perimeter at ×20,000. For morphometric analysis, images were combined to reconstruct the perimeter of a cell. The size and number of transferrin-HRP-labelled organelles up to 1 μm distance from the plasma membrane were quantified. The number of Tf-HRP endosomes was normalized to the plasma membrane perimeter. Ten cells per condition were analysed. To calculate the size of Tf-HRP endosomes, 52 endosomes for scrambled siRNA-treated controls and 90 endosomes for MTM1-depleted cells from a total of 10 cells were analysed. Data are presented as mean values ± s.e.m. A statistical test was performed using a one-sample t-test. For analysis of immunocytochemistry experiments, a minimum of three independent experiments (n) was performed and statistically significant estimates for each sample were obtained by choosing an appropriate sample size, correlating to 15–30 images per condition per experiment for microscopy-based quantifications. Cells were chosen arbitrarily according to the fluorescent signal in a separate channel, which was not used for quantification. Data are presented as mean values ± s.e.m. For analysis of exocytic events per unit area in live-cell TIRF imaging experiments, a minimum of ten videos (duplicate coverslips: five videos per coverslip) per condition per experiment were acquired and fusion events were counted in minimally five videos per condition per experiment. A minimum of three independent experiments were performed and data represent mean values ± s.e.m. All statistical tests were performed using a two-tailed, unpaired t-test, without excluding samples from statistical analysis.


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Protected areas such as rainforests occupy more than one-tenth of the Earth’s landscape, and provide invaluable ecosystem services, from erosion control to pollination to biodiversity preservation. They also draw heat-trapping carbon dioxide (CO ) from the atmosphere and store it in plants and soil through photosynthesis, yielding a net cooling effect on the planet. Determining the role protected areas play as carbon sinks — now and in decades to come — is a topic of intense interest to the climate-policy community as it seeks science-based strategies to mitigate climate change. Toward that end, a study in the journal Ambio estimates for the first time the amount of CO sequestered by protected areas, both at present and throughout the 21st century as projected under various climate and land-use scenarios. Based on their models and assuming a business-as-usual climate scenario, the researchers projected that the annual carbon sequestration rate in protected areas will decline by about 40 percent between now and 2100. Moreover, if about one-third of protected land is converted to other uses by that time, due to population and economic pressures, carbon sequestration in the remaining protected areas will become negligible. “Our study highlights the importance of protected areas in slowing the rate of climate change by pulling carbon dioxide out of the atmosphere and sequestering it in plants and soils, especially in forested areas,” said Jerry Melillo, the study’s lead author. Melillo is a distinguished scientist at the Marine Biological Laboratory (MBL) in Woods Hole, Massachusetts, and former director of the MBL’s Ecosystems Center. “Maintaining existing protected areas, enlarging them and adding new ones over this century are important ways we can manage the global landscape to help mitigate climate change.” Based on a global database of protected areas, a reconstruction of global land-use history, and a global biogeochemistry model, the researchers estimated that protected areas currently sequester 0.5 petagrams (500 billion kilograms) of carbon each year, or about 20 percent of the carbon sequestered by all land ecosystems annually. Using an integrated modeling framework developed by the MIT Joint Program on the Science and Policy of Global Change, they projected that under a rapid climate-change scenario that extends existing climate policies; keeps protected areas off-limits to development; and assumes continued economic growth and a 1 percent annual increase in agricultural productivity, the annual carbon sequestration rate in protected areas would fall to about 0.3 petagrams of carbon by 2100. When they ran the same scenario but allowed for possible development of protected areas, they projected that more than one-third of today’s protected areas would be converted to other uses. This would reduce carbon sequestration in the remaining protected areas to near zero by the end of the century. (The protected areas that are not converted would be the more marginal systems that have low productivity, and thus low capacity to sequester carbon.) Based on this analysis, the researchers concluded that unless current protected areas are preserved and expanded, their capacity to sequester carbon will decline. The need for expansion is driven by climate change: As the average global temperature rises, so, too, will plant and soil respiration in protected and unprotected areas alike, thereby reducing their ability to store carbon and cool the planet. “This work shows the need for sufficient resources dedicated to actually prevent encroachment of human activity into protected areas,” said John Reilly, one of the study’s coauthors and the co-director of the MIT Joint Program on the Science and Policy of Global Change. The study was supported by the David and Lucille Packard foundation, the National Science Foundation, the U.S. Environmental Protection Agency, and the U.S. Department of Energy.


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Measurements on board the C-130 aircraft during the NOMADSS field campaign included HONO, pNO , NO , O , BrO, IO, OH radicals, HO radicals, RO radicals, aerosol surface area densities (for particle diameter <1 μm), VOCs, photolysis frequencies, and other meteorology parameters. Extended Data Table 1 summarizes the instrumentation, time resolution, detection limit, accuracy and references31, 32, 33, 34, 35, 36, 37, 38, 39, 40 for our measurements. HONO was measured by two long-path absorption photometric (LPAP) systems based on the Griess–Saltzman reaction31. Briefly, ambient HONO was scrubbed by deionized water in a 10-turn glass coil sampler. The scrubbed nitrite was then derivatized with 5 mM sulfanilamide (SA) and 0.5 mM N-(1-naphthyl)-ethylene-diamine (NED) in 40 mM HCl, to form an azo dye within 5 min. The azo dye was detected by light absorbance at 540 nm using a fibre optic spectrometer (LEDSPEC-4, World Precision Instruments) with a 1-m liquid waveguide capillary flow cell (World Precision Instruments). ‘Zero-HONO’ air was generated by pulling ambient air through a Na CO -coated denuder to remove HONO and was sampled by the systems periodically to establish measurement baselines. Interference from NO , peroxyacetyl nitrate (PAN) and particulate nitrite, if any, was corrected by subtracting the baseline from the ambient air signal. Owing to the low collection efficiency of these interfering species in the sampling coil and their low concentrations, the combined interference signal was estimated to be less than 10% of the total signal in the clean MBL. Potential interference from peroxynitric acid (HO NO ) was suppressed by heating the Teflon perfluoroalkoxy alkanes (PFA) sampling line to 50 °C with a residence time of 0.8 s. The HO NO steady-state concentration in the MBL was estimated to be less than 1 p.p.t.v. at a temperature of 23–26 °C in both flights, and thus interference from HO NO was negligible41. Overall, the instrument baseline in the clean MBL was stable and low, and clear and strong ambient air signals (approximately ten times the detection limit) were observed. The accuracy of HONO measurements was confirmed by comparison with limb-scanning differential optical absorption spectroscopy (DOAS)36. The agreement between these two instruments was very good in wide power plant plumes, where HONO mixing ratios exceeded the lower detection limits of both instruments (Extended Data Fig. 3). HNO and pNO were quantitatively collected with a coil sampler and a frited glass disk sampler, respectively. The collected nitrate in the two channels were reduced to nitrite by two cadmium (Cd) columns, and determined using two LPAP systems31, 32. Zero air was generated to establish measurement baselines: for HNO by passing the ambient air through a NaCl-coated denuder to remove HNO , and for pNO through a Teflon filter and a NaCl-coated denuder to remove aerosol particles and HNO . Potential interference from HONO, NO and PAN was corrected by subtracting the baselines from the ambient air signals. Ozone measurements were unavailable on 8 July 2013 and OH radical measurements were unavailable on 5 July 2013 owing to instrument malfunction. Steady-state OH radical concentrations were calculated and used in the budget analysis when OH radical measurements were not available42. Most of the parameters were observed at similar values during both flights, indicating that both flights captured the primary features of the local chemical environment. The lack of OH and O measurements on the different flights had a negligible impact on our analysis. Several spikes in NO and aerosol surface area density detected from ship exhaust were excluded from the analysis. Seventy-two-hour back-trajectories were calculated for both flights (Extended Data Fig. 1) with the Lagrangian particle dispersion model FLEXPART43, version 9.02 (http://flexpart.eu), using six-hourly meteorological analysis data of the Global Forecasting System of the National Centers for Environmental Prediction (http://nomads.ncep.noaa.gov/txt_descriptions/GFS_half_degree_doc.shtml), interlaced with 3-h forecasts (0:00 utc, 3:00 utc, 6:00 utc, 9:00 utc, 12:00 utc, 15:00 utc, 18:00 utc and 21:00 utc), at a horizontal resolution of 0.5°. Every 5 min during the research flight 10,000 particles were released at the then-current position of the NSF/NCAR C-130 and followed back in time for 72 h. A ‘particle’ here refers to an infinitesimally small parcel of air, which is only affected by three-dimensional transport, turbulence and convection, and does not have any removal processes (no deposition, washout, sedimentation, chemical losses). Centroids shown in the figures are based on an algorithm44 that reduces the residence probability distribution resulting from the locations of the 10,000 particles into five probable locations at each time interval. One aerosol sample was collected using a Teflon filter (Sartorius, pore size 0.45 μm, diameter 47 mm) on-board the NSF/NCAR C-130 aircraft during every research flight from 30 min after takeoff to 30 min before landing. The total sampling volume ranged from 1.1 m3 to 1.5 m3 depending on the flight length (ranging from 6 h to 8 h). The filter sample was wrapped in aluminium foil and stored in a refrigerator until use. The pNO photolysis rate constant was determined using the filter sample in the laboratory. The photochemical experiments were conducted using a cylindrical flow reactor (inner diameter 10 cm, depth 1.5 cm) with a quartz window on the top, at a temperature of 21.0 ± 1.0 °C and a relative humidity of (50 ± 2)%. The filter sample was moved into the flow cell directly from the freezer for the photochemical experiment. Compressed air was purified by flowing through an activated charcoal and Purafil chemisorbant column (Purafil) to remove NO , VOCs, H S, SO , HNO and HONO and was used as carrier gas. Gaseous products, HONO and NO , released during the experiment were flushed out of the reactor by the carrier gas, and were sampled by two coil samplers connected in series. The first 10-turn coil sampler scrubbed HONO with purified water at a collection efficiency of 100% (ref. 31), and the second 32-turn coil sampler was to scrub NO with an acetic acid modified SA/NED solution at a collection efficiency of 60% (ref. 45). The scrubbed nitrite and NO were converted to an azo dye with SA/NED reagents and analysed by two separate LPAP systems31, 45. The filter sample was exposed to the solar simulator radiation for 10 min; baselines were established for both HONO and NO before and after the light exposure. Photochemical production rates of HONO and NO were calculated from their time-integrated signals above the baselines over the period of light exposure. To correct for HONO and NO production from photolysis of HNO deposited on the flow reactor wall surface, a control experiment was conducted by irradiating the empty flow reactor. The control signals were subtracted from the sample exposure signals when calculating the production rates of HONO and NO from pNO photolysis. After 10 min of light exposure, nitrate in the filter sample was extracted with 15 ml 1% NH Cl buffer solution (pH = 8.5), reduced to nitrite by a Cd column and determined by LPAP. A 300-W Ceramic xenon arc lamp (Perkin Elmer, model PE300BUV) was used as a light source. The ultraviolet light below 290 nm and the heat-generating infrared light were removed by a Pyrex glass filter and a water filter, respectively. The parabolic light beam irradiated only a circular area of a 1-inch (2.54 cm) diameter in the centre of the flow reactor. The shape of the spectrum of the filtered light source is similar to that of the solar actinic spectrum in the MBL (Extended Data Fig. 4). The effective light intensity in the centre of the flow reactor under direct irradiation was measured to be about 3.5 times higher than that at tropical noon on the ground (solar elevation angle θ = 0°)1, 18, using a nitrate actinometer18. The production rates of HONO and NO were normalized by the amount of pNO exposed and the effective ultraviolet light intensity, to obtain the normalized photolysis rate constants of pNO , (ref. 5). where P and are the production rates of HONO and NO , respectively; N is the light-exposed pNO amount determined in the extraction solution; J is the photolysis rate constant of nitrate in the actinometer solution exposed to the experimental light source; and the ‘standard’ photolysis rate constant of aqueous nitrate (J  ≈ 3.0 × 10−7 s−1) is assumed for typical tropical summer conditions on the ground (solar elevation angle θ = 0°)1, 18, corresponding to a gas-phase HNO photolysis rate constant ( ) of ~7.0 × 10−7 s−1. HONO is the major product from pNO photolysis, with an average production ratio of HONO to NO of 2.0. It should be noted that the value is calculated from the production rates of HONO and NO , and thus it may underestimate the photolysis rate constant of pNO if other products, such as NO, are produced. However, NO was only a minor secondary product from the photolysis of HONO and NO , and accounted for ~1% of the total production of HONO and NO in the flow reactor in a resident time of 8 s. Therefore, lack of NO measurement in this study would not affect the measurement. The overall uncertainty in the photolysis rate constant measurement is about 50%, taking into account the measurement uncertainties in production rates of HONO and NO , nitrate loading and effective ultraviolet light intensity. It should be pointed out that the laboratory-determined represents merely an average photolysis rate constant of pNO collected during the entire flight covering a wide geographic area, and thus does not reflect the possible temporal and spatial variability in the actual during the flight. In addition, the photochemical reactivity of bulk aerosol sample collected on the filter may be altered during sampling and handling, and thus the laboratory-determined using the bulk aerosol sample might be different from that under the ambient conditions. Nevertheless, the laboratory-determined is still the first and best estimate of the pNO photolysis rate constant in the ambient atmosphere so far, and is in reasonable agreement with the values inferred from field observations (Fig. 2). To evaluate the global recycling NO source from particulate nitrate photolysis, more aerosol samples need to be collected from different atmospheric environments all over the world, and better-designed experiments need to be conducted using these samples to more accurately determine the photolysis rates constants of particulate nitrate under conditions similar to those of ambient atmosphere. The air masses encountered consisted mostly of aged air masses circulating within the boundary layer of the North Atlantic Ocean under a Bermuda high-pressure system for several days before reaching the measurement locations (Extended Data Fig. 1). Within this relatively isolated air mass in the MBL, the cycling of pNO –HONO–NO was occurring much more rapidly, of the order of hours during the day, than dry deposition, which was of the order of days. Therefore, the diurnal chemistry of pNO –HONO–NO in the MBL can be simulated by a zero-dimensional box model. Model simulations were conducted using the MCM v3.2 updated with the Jet Propulsion Laboratory’s latest recommended kinetics10, 11, 46, and constrained by the field-measured long-lived species, such as O , VOCs and total NO . The model was initiated from 00:00 local time and was allowed to run for three diurnal cycles (72 h). The diurnal concentration profiles of short-lived species, including OH, HO , HONO and NO , were affected by their initial concentrations only during the first diurnal cycle of simulation. The results presented here (Fig. 3, Extended Data Figs 2 and 5) were from the second diurnal cycle of the simulation run. To simulate the time-varying cycling of pNO –HONO–NO , the diurnal profiles of photolysis frequencies were also calculated by the MCM v3.2 and scaled by the measured values in the early afternoon. The time-dependent photolysis rate constant of particulate nitrate ( ) at certain times of the day was calculated as follows: where is the photolysis rate constant of gas-phase HNO measured in the MBL. The ratio of pNO to HNO was set to 1, as observed in the MBL during the early afternoon. The photolysis of pNO is considered a source of both HONO and NO . To include all the possible HONO sources in the model, the following reactions are also considered: the gas-phase reactions of OH with NO, of excited NO with H O (refs 26 and 27), and of HO ·H O with NO (refs 16 and 28), and the heterogeneous reactions of NO on sea-salt aerosol particles. Upper-limit HONO formation rates from reactions of excited NO with H O and of HO ·H O with NO are calculated using the latest recommendations27, 28. An upper-limit uptake coefficient of 10−4 was assumed for NO uptake on sea-salt aerosol47. HONO sinks in the model include its photolysis and the gas-phase reaction with OH radicals. The photolysis of pNO is a NO source mainly through HONO as an intermediate. Other NO sources, such as the photolysis of gaseous HNO and the reaction of gaseous HNO with OH, are negligible, and are not included here. NO sinks include the formation of bromine nitrate (BrONO ), iodine nitrate (IONO ) and organic nitrate (RONO ) as well as HNO through the reactions of NO with OH and of NO with HO . The uptake of halogen nitrate and organic nitrates on the sea-salt aerosols and following hydrolysis provide effective pathways for conversion of NO to particulate nitrate in addition to HNO uptake. The photolysis lifetime of BrONO in the gas phase was estimated to be ~12 min in the MBL, somewhat longer than the uptake and hydrolysis lifetime of ~6 min using an uptake coefficient of 0.8 on sea-salt aerosols46. Therefore, two-thirds of BrONO is converted to pNO through hydrolysis on the aerosol particle at midday. The photolysis lifetime of IONO in the gas phase was estimated to be ~5 min (ref. 48), comparable to its uptake and hydrolysis lifetime46, 49. Therefore, half of IONO is converted to pNO via hydrolysis on the aerosol particles at midday. BrO was measured at 1.5 p.p.t.v. by the DOAS instrument. The IO level was below the detection limit and was assumed to be at the typical MBL concentration49, 50 of 0.5 p.p.t.v. To evaluate the impact of VOCs on reactive nitrogen chemistry, explicit methane chemistry is considered in the model. Higher VOCs are lumped into a single species, RH. The concentration of RH is adjusted by the sum of the abundance of individually measured VOCs (R H) scaled by the ratio of their rate coefficients for OH+R H to that for OH+CH . The products of OH+RH reactions lead to the formation of RO radicals, carbonyls, peroxides, and organic nitrates. The yield of organic nitrate from the reaction of RO radical with NO (reaction 3) is calculated based on the reaction rate of CH O radical with NO ( ) and the effective branching ratio β. where [R O ] and [CH O ] are the concentrations of various R O radicals formed from oxidation of various VOCs and methane, respectively; k and β are the recommended rate constants and branching ratios (β ) for various RO radicals, respectively30, 51. The effective branching ratio β is estimated to be ~7% for our model. The fate of RONO in the MBL is not well known; hydrolysis has been assumed to be an effective sink of organic nitrates in forested regions30. In our model, an uptake coefficient of 10−2 and a 100% nitrate yield from RONO uptake is assumed for organic nitrates on sea-salt aerosol. The model is initialized with typically measured parameters and is run at 15-min time steps for three diurnal cycles. Multiple runs are used to constrain the model uncertainty by error propagation from the errors of all considered parameters. Gaussian error propagation is applied for the uncertainty calculation. Overall, a model uncertainty of 28% (±1 s.d.) is calculated. The reactive nitrogen species are found to be reproducible for multiple days without considering deposition and transport. Sensitivity studies with the model indicate that the calculated HONO concentrations are relatively insensitive (≤2%) to the following assumed parameters: the RONO branching ratio (varying from 1% to 7% to 14%), the uptake coefficients of RONO and XONO (changing up and down by a factor of two), the nitrate yield from RONO hydrolysis (varying from 5% to 100%), the initial concentration of non-methane hydrocarbons (changing up and down by a factor of two), and the RO +NO reaction rate constant (changing up and down by a factor of two). Changing up and down by a factor of two in the initial concentrations of BrO or IO leads to a change of up to 4% in the calculated HONO. Finally, the sensitivity of the model output to the ratio of HNO to pNO was assessed by varying between 0.5 and 2.0, and the modelled HONO varied between 1.17 and 0.74 of the value for the ratio of unity. Despite the simplicity of this model approach, the good agreement between model calculations and field observations for key species (Fig. 3 and Extended Data Fig. 5) shows its feasibility and usefulness for assessing the chemistry of radicals and reactive nitrogen in the MBL.


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This week, for the first time, scientists describe distinct bacterial assemblages living in dental plaque, which they discovered using a novel imaging approach that "cuts through the overwhelming complexity of detail in microbial communities and allows common patterns to shine through." The study appears in Proceedings of the National Academy of Sciences and was led by Jessica Mark Welch of the Marine Biological Laboratory (MBL), Woods Hole, and Gary Borisy of the Forsyth Institute, Cambridge. Plaque on teeth, the team discovered, contains micron-scaled "hedgehog" structures in which eight different kinds of bacteria are radially arranged around ninth kind, filamentous Corynebacteria. Seeing these structures offers scientists valuable information on how the bacterial members function that can't be gleaned from genomic analysis, which specifies what microbes are present in a community, but not how they are organized. "Microbes behave very differently depending on where they are and who they are next to," Mark Welch says. "They will secrete entirely different sets of chemicals and metabolites depending on who their microbial neighbors are. So, if we want to accurately describe what these microbes are doing - really, what they are - we need to know where they are." The team proposes a model for how dental plaque develops, which is based on their imaging observations combined with plaque sequencing data from the Human Microbiome Project. "This is a really exciting new way to look at microbial communities," Mark Welch says of the spectral fluorescence imaging approach they developed at MBL. "The degree of organization we found in the hedgehog structure was amazing, as was the repeated finding of the same structure in different individuals. This finding that bacteria can develop such a degree of spatial organization may be generalizable to other microbiomes. We just have to go look." Explore further: Choosing your neighbors: Scientists see how microbes relate in space More information: Biogeography of a human oral microbiome at the micron scale, www.pnas.org/cgi/doi/10.1073/pnas.1522149113

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