39,306 research outputs found

    Proteomic and 3D structure analyses highlight the C/D box snoRNP assembly mechanism and its control

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    In vitro, assembly of box C/D small nucleolar ribonucleoproteins (snoRNPs) involves the sequential recruitment of core proteins to snoRNAs. In vivo, however, assembly factors are required (NUFIP, BCD1, and the HSP90–R2TP complex), and it is unknown whether a similar sequential scheme applies. In this paper, we describe systematic quantitative stable isotope labeling by amino acids in cell culture proteomic experiments and the crystal structure of the core protein Snu13p/15.5K bound to a fragment of the assembly factor Rsa1p/NUFIP. This revealed several unexpected features: (a) the existence of a protein-only pre-snoRNP complex containing five assembly factors and two core proteins, 15.5K and Nop58; (b) the characterization of ZNHIT3, which is present in the protein-only complex but gets released upon binding to C/D snoRNAs; (c) the dynamics of the R2TP complex, which appears to load/unload RuvBL AAA+ adenosine triphosphatase from pre-snoRNPs; and (d) a potential mechanism for preventing premature activation of snoRNP catalytic activity. These data provide a framework for understanding the assembly of box C/D snoRNPs

    MADS-box genes controlling inflorescence morphogenesis in sunflower

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    MADS-box genes play an important role in plant ontogeny, particularly, in the regulation of floral organ induction and development. Eight full-length cDNAs of HAM genes (Helianthus annuus MADS) have been isolated from sunflower. They encode MADS-box transcription factors expressed in inflorescence tissues. In the frames of the ABCDE model, the HAM proteins were classified according to their structural homology to known MADS-box transcription factors. The HAM45 and HAM59 genes encode the homeotic C function and are involved in the control of the identity of pistil and stamens, while the HAM75 and HAM92 genes determine the A function and identity of floral and inflorescence meristems and petal identity. The HAM31, HAM2, HAM63, and HAM91 genes encode the B function and are involved in the formation of petals and stamens; and the HAM137 gene encodes the E function. Analysis of the expression of HAM genes in sunflower has demonstrated that the structural and functional differences between the ray and tubular flowers in the inflorescence could be a consequence of the lack of HAM59 expression during ray flower initiation

    Recognition of two distinct elements in the RNA substrate by the RNA-binding domain of the T. thermophilus DEAD box helicase Hera

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    DEAD box helicases catalyze the ATP-dependent destabilization of RNA duplexes. Whereas duplex separation is mediated by the helicase core shared by all members of the family, flanking domains often contribute to binding of the RNA substrate. The Thermus thermophilus DEAD-box helicase Hera (for “heat-resistant RNA-binding ATPase”) contains a C-terminal RNA-binding domain (RBD). We have analyzed RNA binding to the Hera RBD by a combination of mutational analyses, nuclear magnetic resonance and X-ray crystallography, and identify residues on helix α1 and the C-terminus as the main determinants for high-affinity RNA binding. A crystal structure of the RBD in complex with a single-stranded RNA resolves the RNA–protein interactions in the RBD core region around helix α1. Differences in RNA binding to the Hera RBD and to the structurally similar RBD of the Bacillus subtilis DEAD box helicase YxiN illustrate the versatility of RNA recognition motifs as RNA-binding platforms. Comparison of chemical shift perturbation patterns elicited by different RNAs, and the effect of sequence changes in the RNA on binding and unwinding show that the RBD binds a single-stranded RNA region at the core and simultaneously contacts double-stranded RNA through its C-terminal tail. The helicase core then unwinds an adjacent RNA duplex. Overall, the mode of RNA binding by Hera is consistent with a possible function as a general RNA chaperone

    Interactions between the box tree moth Cydalima perspectalis (Walker) and the tachinid parasitoid Exorista larvarum (L.).

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    Cydalima perspectalis (Walker) (Lepidoptera Crambidae), native to East Asia, was first recorded in Europe in 2007. In Italy, it was detected in 2010 in Veneto and it is now distributed in other regions. This exotic species represents a threat to Buxus plants in European parks and gardens, as well as in natural environments, i.e. spontaneous formations of southern France and northwestern Italy. In Europe, only two parasitoids, including the tachinid Pseudoperichaeta nigrolineata (Walker), have so far been found to attack C. perspectalis in nature, at a very low rate. In the areas of origin, the parasitoid complex of the box tree moth is wider and comprises other tachinid species, including Exorista spp. A stock colony of Exorista larvarum (L.), a larval parasitoid native to the Palearctic region, is currently maintained in the laboratory of Entomology of the University of Bologna, using Galleria mellonella (L.) (Lepidoptera Pyralidae) as a factitious host. Both biological bioassay and anatomical and histological examinations were carried out to evaluate the possibility of adaptation of this indigenous tachinid species to C. perspectalis. In no-choice experiments, box tree moth larvae were accepted by E. larvarum females, though a lower number of eggs were laid compared to G. mellonella, maintained as a control. Most eggs hatched, as also shown in the anatomical and histological studies, but no puparia formed in any accepted C. perspectalis larva. Two out of six first instar E. larvarum larvae penetrated the body of a box tree moth larva and were encapsulated. The encapsulation response turned into the formation of the respiratory funnel by two parasitoid larvae, similarly to what happens in G. mellonella. The results obtained in this study showed that C. perspectalis was unsuitable as host for E. larvarum. The mortality following the parasitoid larval activity (independently of successful parasitism) was, however, not significantly different between C. perspectalis and G. mellonella. The overall results suggest that the mortality of C. perspectalis larvae due to the partial development of E. larvarum may be useful to regulate the populations of this invasive pest in a context of conservative biological control

    Analysis of a hybrid TATA box binding protein originating from mesophilic and thermophilic donor organisms

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    The TATA Box Binding Protein (TBP) is a 20 kD protein that is essential and universally conserved in eucarya and archaea. Especially among archaea, organisms can be found that live below 0°C as well as organisms that grow above 100°C. The archaeal TBPs show a high sequence identity and a similar structure consisting of α-helices and β-sheets that are arranged in a saddle-shape 2-symmetric fold. In previous studies, we have characterized the thermal stability of thermophilic and mesophilic archaeal TBPs by infrared spectroscopy and showed the correlation between the transition temperature (Tm) and the optimal growth temperature (OGT) of the respective donor organism. In this study, a “new” mutant TBP has been constructed, produced, purified and analyzed for a deeper understanding of the molecular mechanisms of thermoadaptation. The β-sheet part of the mutant consists of the TBP from Methanothermobacter thermoautotrophicus (OGT 65°C, MtTBP65) whose α-helices have been exchanged by those of Methanosarcina mazei (OGT 37°C, MmTBP37). The Hybrid-TBP irreversibly aggregates after thermal unfolding just like MmTBP37 and MtTBP65, but the Tm lies between that of MmTBP37 and MtTBP65 indicating that the interaction between the α-helical and β-sheet part of the TBP is crucial for the thermal stability. The temperature stability is probably encoded in the variable α-helices that interact with the highly conserved and DNA binding β-sheets

    Greenland SMB, D and TMB annual time series 1840-2012

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    <body lang=en-DK link=blue vlink="#954F72" style='tab-interval:36.0pt; word-wrap:break-word'> All-Greenland Surface and Total Mass Balance annual time series after <li class=MsoNormal style='color:black;mso-list:l2 level1 lfo3;tab-stops:list 36.0pt; vertical-align:baseline'>Kjeldsen et al (2015) <span style='color:#1155CC'>https://doi.org/10.1038/nature16183&nbsp; <li class=MsoNormal style='color:black;mso-list:l2 level1 lfo3;tab-stops:list 36.0pt; vertical-align:baseline'>Box (2013) SMB <span style='color:#1155CC'>https://doi.org/10.1175/JCLI-D-12-00518.1 <li class=MsoNormal style='color:black;mso-list:l2 level1 lfo3;tab-stops:list 36.0pt; vertical-align:baseline'>Box and Colgan (2013) TMB <span style='color:#1155CC'>https://doi.org/10.1175/jcli-d-12-00546.1 <li class=MsoNormal style='color:black;margin-bottom:4.0pt;mso-list:l2 level1 lfo3; tab-stops:list 36.0pt;vertical-align:baseline'><span style='mso-fareast-font-family: "Times New Roman"'>Box et al. (2013) Accumulation <a href="https://doi.org/10.1175/JCLI-D-12-00373.1">https://doi.org/10.1175/JCLI-D-12-00373.1 Data file and notes <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>Greenland_mass_balance_totals_1840-2012_ver_20141130_with_uncert_via_Kjeldsen_et_al_2015.csv <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>Column headers:&nbsp; <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>year&nbsp;&nbsp;&nbsp;&nbsp; accumulation&nbsp; accumulation 1sigma&nbsp; melt&nbsp;&nbsp;&nbsp;&nbsp; melt 1sigma&nbsp;&nbsp;&nbsp; retention&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; retention 1sigma&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; runoff&nbsp; runoff 1sigma discharge from 6 year lagged average runoff&nbsp;&nbsp;&nbsp;&nbsp; discharge 1sigma&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; TMB&nbsp;&nbsp;&nbsp; TMB 1sigma <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>1840&nbsp;&nbsp;&nbsp; 645.43 65.82&nbsp;&nbsp; 277.70 64.34&nbsp;&nbsp; 143.07 48.72&nbsp;&nbsp; 173.56 46.15&nbsp;&nbsp; 406.08 36.65&nbsp;&nbsp; 65.79 <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>Units: Gt per year, temperature in deg. C&nbsp; <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>Column description: “1sigma” refers to uncertainty; “accumulation” is snow accumulation equivalent with tp minus vapor lsos; “melt” is snow or ice converted to liquid; “retention” is nternal accumulation; “runoff” is liquid melt water exiting ice sheet; “SMB” is surface mass balance; “TMB” is total mass balance <p class=MsoNormal style='margin-top:0cm;margin-right:0cm;margin-bottom:4.0pt; margin-left:36.0pt'>From these data SMB can be computed as: accumulation - runoff - discharge Time series visualization code and data: <a href="https://github.com/jasonebox/TMB_Greenland_1840-2012"><span style='font-family:"Calibri",sans-serif;color:#1155CC'>https://github.com/jasonebox/TMB_Greenland_1840-2012 Issues: <a href="https://github.com/jasonebox/TMB_Greenland_1840-2012/issues"><span style='font-family:"Calibri",sans-serif;color:#1155CC'>https://github.com/jasonebox/TMB_Greenland_1840-2012/issues Description The Box<span lang=EN-US style='color:black;mso-ansi-language:EN-US'> (<span style='color:black'>2013) 171 year (1840-2010) surface mass balance reconstruction is developed from linear regression parameters that describe the correlation between a.) spatially discontinuous in-situ monthly air temperature records (Cappelen, 2011; Cappelen et al., 2001, 2006; Vinther et al., 2006) or firn/ice cores (Box et al., 2013) and b.) spatially continuous outputs from regional climate model RACMO version 2.1 (Ettema et al., 2010). A 43-year overlap period 1960–2012 with RACMO2.1 is used to determine regression parameters on a 5 km grid cell basis. Then the predictor (air temperature and firn/ice core) data span 1840 to 2012. A fundamental assumption is that the calibration factors, regression slope and offset for the calibration period 1960–2012 are stationary over time. See “part I” of Box et al. (2013) for a description of the method, which includes a formal approach to estimate uncertainty. The Box<span lang=EN-US style='color:black;mso-ansi-language:EN-US'> (<span style='color:black'>2013) 171 year (1840-2010) SMB reconstruction is refined in (Kjeldsen et al., 2015) to incorporate: including peripheral ice masses in addition to the ice sheet; a more sophisticated meltwater retention scheme (Pfeffer et al., 1991); multiple in-situ records are weighted in their contribution to the estimated value; the annual accumulation rates from ice cores are dispersed <span style='color:black'>into a monthly temporal resolution by weighting the monthly (based on the 1960–2012 RACMO2.1 data) fraction of the annual total for each grid cell in the domain and the revised surface mass balance data end with year 2012. The 173 year (1840-2012) reconstruction of annual total mass balance (TMB) is after (Box and Colgan, 2013) improved in (Kjeldsen et al., 2015). Annual solid ice discharge<span style='color:black'> (<span lang=EN-US style='color:black;mso-ansi-language: EN-US'>D) was estimated via a fit of unsmoothed solid ice discharge data (Rignot et al., 2008, 2011) with Box<span lang=EN-US style='color:black;mso-ansi-language:EN-US'> (<span style='color:black'>2013) runoff data having a 6-year trailing average in Kjeldsen et al. (<span style='color:black'>2015). The physical basis for the SID parameterization using runoff is described in (Box and Colgan, 2013). Works Cited <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Box, J. E.: Greenland Ice Sheet Mass Balance Reconstruction. Part II: Surface Mass Balance (1840–2010), J. Clim., 26(18), 6974–6989, 2013. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Box, J. E. and Colgan, W.: Greenland Ice Sheet Mass Balance Reconstruction. Part III: Marine Ice Loss and Total Mass Balance (1840–2010), J. Clim., 26(18), 6990–7002, 2013. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Box, J. E., Cressie, N., Bromwich, D. H., Jung, J.-H., van den Broeke, M., van Angelen, J. H., Forster, R. R., Miège, C., Mosley-Thompson, E., Vinther, B. and McConnell, J. R.: Greenland Ice Sheet Mass Balance Reconstruction. Part I: Net Snow Accumulation (1600–2009), J. Clim., 26(11), 3919–3934, 2013. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cappelen, J.: DMI monthly climate data collection 1768– 2010, Denmark, the Faroe Islands and Greenland, Danish Meteorological Institute., 2011. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cappelen, J., Jørgensen, B. V., Laursen, E. V., Stannius, L. S. and Thomsen, R. S.: The observed climate of Greenland, 1958–99 with climatological standard normals, Danish Meteorological Institute., Technical Report 00-18, 151 pp., 2001. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cappelen, J., Laursen, E. V., Jørgensen, P. V. and Kern-Hansen, C.: DMI monthly climate data collection 1768–2005, Denmark, the Faroe Islands and Greenland, Danish Meteorological Institute., 2006. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ettema, J., van den Broeke, M. R., van Meijgaard, E., van de Berg, W. J., Box, J. E. and Steffen, K.: Climate of the Greenland ice sheet using a high-resolution climate model – Part 1: Evaluation, The Cryosphere, 4(4), 511–527, 2010. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Kjeldsen, K. K., Korsgaard, N. J., Bjørk, A. A., Khan, S. A., Box, J. E., Funder, S., Larsen, N. K., Bamber, J. L., Colgan, W., van den Broeke, M., Siggaard-Andersen, M.-L., Nuth, C., Schomacker, A., Andresen, C. S., Willerslev, E. and Kjær, K. H.: Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900, Nature, 528(7582), 396–400, 2015. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pfeffer, W. T., Meier, M. F. and Illangasekare, T. H.: Retention of Greenland runoff by refreezing: Implications for projected future sea level change, J. Geophys. Res., 96, 22,117, 1991. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rignot, E., Box, J. E., Burgess, E. and Hanna, E.: Mass balance of the Greenland ice sheet from 1958 to 2007, Geophysical Research Letters, 35(20), doi:10.1029/2008gl035417, 2008. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rignot, E., Velicogna, I., van den Broeke, M. R., Monaghan, A. and Lenaerts, J. T. M.: Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise, Geophys. Res. Lett., 38(5), doi:10.1029/2011gl046583, 2011. <span style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol'>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Vinther, B. M., Andersen, K. K., Jones, P. D., Briffa, K. R. and Cappelen, J.: Extending Greenland temperature records into the late eighteenth century, J. Geophys. Res., 111(D11), doi:10.1029/2005jd006810, 2006.&nbsp;&nbsp; </html

    A constructive algebraic strategy for interpolatory subdivision schemes induced by bivariate box splines

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    This paper describes an algebraic construction of bivariate interpolatory subdivision masks induced by three-directional box spline subdivision schemes. Specifically, given a three-directional box spline, we address the problem of defining a corresponding interpolatory subdivision scheme by constructing an appropriate correction mask to convolve with the three-directional box spline mask. The proposed approach is based on the analysis of certain polynomial identities in two variables and leads to interesting new interpolatory bivariate subdivision schemes

    Antarctica SSA and broadband albedo austral summer 2022/2023 from Sentinel-3’s OLCI and pySICEv1.6

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    Timespan 15 Oct, 2022 to 28 Feb 2023 Description 0.5 km daily SSA and broadband albedo. Albedo is &#39;spherical&#39;, meaning &#39;white sky&#39; or &#39;diffuse&#39;, i.e. does not depend on the position of the sun. Data format is NetCDF. Embedded are attributes. We suggest using xarray to read the data files. pySICEv1.6 is available at&nbsp; https://github.com/GEUS-SICE/pySICE/releases/tag/v1.6 Related Publications Kokhanovsky, A., M. Lamare, O. Danne, C. Brockmann, M. Dumont, G. Picard, L. Arnaud, V. Favier, B. Jourdain, E. Lemeur, B. Di Mauro, T Aoki, M. Niwano, V. Rozanov, S. Korkin, S. Kipfstuhl, J. Freitag, M. Hoerhold, A. Zuhr, D. Vladimirova, A.-K. Faber, H.C. Steen-Larsen, S. Wahl, J.K. Andersen, B. Vandecrux, D. van As, K.D. Mankoff, M. Kern, E. Zege, and J.E. Box, Retrieval of snow and ice properties from the Sentinel-3 Ocean and Land Colour Instrument, Remote Sensing, Remote Sens. 2019, 11(19), 2280; https://doi.org/10.3390/rs11192280 Kokhanovsky, A.; Box, J.E.; Vandecrux, B.; Mankoff, K.D.; Lamare, M.; Smirnov, A.; Kern, M. The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique. Remote Sens. 2020, 12, 234. https://doi.org/10.3390/rs12020234 Arioli, S., Picard, G., Arnaud, L., and Favier, V.: Dynamics of the snow grain size in a windy coastal area of Antarctica from continuous in situ spectral-albedo measurements, The Cryosphere, 17, 2323&ndash;2342, https://doi.org/10.5194/tc-17-2323-2023, 2023. Vandecrux, B.; Box, J.E.; Wehrl&eacute;, A.; Kokhanovsky, A.A.; Picard, G.; Niwano, M.; H&ouml;rhold, M.; Faber, A.-K.; Steen-Larsen, H.C. The Determination of the Snow Optical Grain Diameter and Snowmelt Area on the Greenland Ice Sheet Using Spaceborne Optical Observations. Remote Sens. 2022, 14, 932. https://doi.org/10.3390/rs14040932 Questions? [email protected] New pySICE versions Find the latest version of pySICE here: https://github.com/GEUS-SICE/pySICE For example pySICEv2.0 described in Kokhanovsky, A., Vandecrux, B., Wehrl&eacute;, A., Danne, O., Brockmann, C., and Box, J. E.: An improved retrieval of snow and Ice properties using spaceborne OLCI/S-3 spectral reflectance measurements: Updated atmospheric correction and snow impurity load estimation, Remote Sens. (Basel), 15, 77, https://doi.org/10.3390/rs15010077, 2022. &nbsp;</p

    N.S.W.G.R. steam locomotive class "C" 3215 with an extended steam box at Central station, 30 December, 1926 [picture] /

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    Condition: Good.; Title from accompanying documentation.; Part of collection: Buckland collection of railway transport photographs.; "N.S.W.G.R. Loco. class "C" 3215 at Central station. This engine is not superheated but has extended smoke-box. Photo: C.S. Edwards 1926" -- in ink on reverse.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.pic-an24768333

    Signal control using vehicle localization probe data

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    This paper presents a simulation test bed and methodology for evaluating urban signalized junction control algorithms that use localization probe data from all vehicles in the local area. The simulator is based on SIAS Paramics micro-simulation software with bespoke software modules built on top for automatic network generation, localization data processing and signal control. Localization algorithms tested use a hierarchical structure of auctioning agents. Early tests of control algorithms on an isolated signalized junction indicate performance that compares favourably with the MOVA algorithm using inductive loop data.<br/
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