351 research outputs found
Design of plasma shutters for improved heavy ion acceleration by ultra-intense laser pulses
In this work, we investigate the application of the plasma shutters for heavy
ion acceleration driven by a high-intensity laser pulse. We use
particle-in-cell (PIC) and hydrodynamic simulations. The laser pulse,
transmitted through the opaque shutter, gains a steep-rising front and its peak
intensity is locally increased at the cost of losing part of its energy. These
effects have a direct influence on subsequent ion acceleration from the
ultrathin target behind the shutter. In our 3D simulations of silicon nitride
plasma shutter and a silver target, the maximal energy of high-Z ions increases
significantly when the shutter is included for both linearly and circularly
polarized laser pulses. Moreover, application of the plasma shutter for
linearly polarized pulse results in focusing of ions towards the laser axis in
the plane perpendicular to the laser polarization. The generated high energy
ion beam has significantly lower divergence compared to the broad ion cloud,
generated without the shutter. The effects of prepulses are also investigated
assuming a double plasma shutter. The first shutter can withstand the assumed
sub-ns prepulse (treatment of ns and ps prepulses by other techniques is
assumed) and the pulse shaping occursvia interaction with the second shutter.
On the basis of our theoretical findings, we formulated an approach towards
designing a double plasma shutter for high-intensity and high-power laser
pulses and built a prototype.Comment: 30 pages 13 figure
Π‘ΠΠ‘Π’ΠΠ Π Π‘ΠΠΠΠ‘Π’ΠΠ ΠΠΠΠΠ Π₯ΠΠΠ‘Π’ΠΠ«Π₯ Π‘ΠΠΠΠ, Π€ΠΠ ΠΠΠ Π£ΠΠΠ«Π₯ ΠΠΠΠΠ-ΠΠ‘Π‘ΠΠ‘Π’ΠΠ Π£ΠΠΠ«Π ΠΠ‘ΠΠΠΠΠΠΠΠ ΠΠΠ’ΠΠΠΠ’ΠΠ§ΠΠ‘ΠΠΠ₯ ΠΠΠ’ΠΠΠΠΠ ΠΠ ΠΠΠΠΠΠ« ΠΠΠΠ£Π£ΠΠΠΠΠ ΠΠ£ΠΠΠΠΠΠ Π ΠΠΠ Π―ΠΠ ΠΠ Π£ΠΠΠΠ ΠΠΠΠ«Π ΠΠΠΠΠΠΠΠ
Surface layers were prepared by ion beam-assisted deposition (IBAD) of platinum and rare earth metals (Ce, Yb) on the carbon-based Toray Carbon Fiber Paper TGP-H-060 T catalyst support in effort to produce electrocatalysts for direct methanol and ethanol oxidation fuel cells (DMFC, DEFC) with polymer electrolyte membrane. The layer formation in the IBAD mode, by means of the metal deposition and the mixing of a precipitating layer with the substrate by accelerated (U = 10 kV) ions of the same metal, was carried out. In this process, a neutral fraction of metal vapor and ionized plasma of vacuum pulsed electric arc was used. The study of morphology and composition of the layers was performed by scanning electron microscopy and electron probe microanalysis, X-ray fluorescence analysis and Rutherford backscattering spectrometry. Properties of the prepared electrocatalysts were investigated by cyclic voltammetry. It was established that the prepared electrocatalysts show their activity in the processes of electrochemical methanol and ethanol oxidation.Β ΠΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠ½ΡΠ΅ ΡΠ»ΠΎΠΈ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Ρ ΠΈΠΎΠ½Π½ΠΎΠ°ΡΡΠΈΡΡΠΈΡΡΠ΅ΠΌΡΠΌ ΠΎΡΠ°ΠΆΠ΄Π΅Π½ΠΈΠ΅ΠΌ (IBAD) ΠΏΠ»Π°ΡΠΈΠ½Ρ ΠΈ ΡΠ΅Π΄ΠΊΠΎΠ·Π΅ΠΌΠ΅Π»ΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠ² (Ce, Yb) Π½Π° Π½ΠΎΡΠΈΡΠ΅Π»Ρ Toray Carbon Fiber Paper TGP-H-060 Π’ Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ°Π»ΠΈΠ·Π°ΡΠΎΡΠΎΠ² Π΄Π»Ρ ΡΠΎΠΏΠ»ΠΈΠ²Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΏΡΡΠΌΠΎΠ³ΠΎ ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠ°Π½ΠΎΠ»Π° ΠΈ ΡΡΠ°Π½ΠΎΠ»Π° Ρ ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠ½ΡΠΌ ΠΌΠ΅ΠΌΠ±ΡΠ°Π½Π½ΡΠΌ ΡΠ»Π΅ΠΊΡΡΠΎΠ»ΠΈΡΠΎΠΌ. Π€ΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ»ΠΎΠ΅Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π² ΡΠ΅ΠΆΠΈΠΌΠ΅ IBAD, ΠΏΡΠΈ ΠΊΠΎΡΠΎΡΠΎΠΌ ΠΎΡΠ°ΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠ°Π»Π»Π° ΠΈ ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠΈΠ²Π°Π½ΠΈΠ΅ ΠΎΡΠ°ΠΆΠ΄Π°Π΅ΠΌΠΎΠ³ΠΎ ΡΠ»ΠΎΡ Ρ Π°ΡΠΎΠΌΠ°ΠΌΠΈ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΏΠΎΠ΄Π»ΠΎΠΆΠΊΠΈ ΡΡΠΊΠΎΡΠ΅Π½Π½ΡΠΌΠΈ (U = 10 ΠΊΠ) ΠΈΠΎΠ½Π°ΠΌΠΈ ΡΠΎΠ³ΠΎ ΠΆΠ΅ ΠΌΠ΅ΡΠ°Π»Π»Π° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡΡΡ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ ΠΈΠ· Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΡΠ°ΠΊΡΠΈΠΈ ΠΏΠ°ΡΠ° ΠΈ ΠΏΠ»Π°Π·ΠΌΡ Π²Π°ΠΊΡΡΠΌΠ½ΠΎΠ³ΠΎ Π΄ΡΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΡΠ°Π·ΡΡΠ΄Π° ΠΈΠΌΠΏΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΡΠ»Π΅ΠΊΡΡΠΎΠ΄ΡΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠΎΡΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΡΠΎΡΡΠ°Π²Π° ΡΠ»ΠΎΠ΅Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΡΠΊΠ°Π½ΠΈΡΡΡΡΠ΅ΠΉ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠΈΠΈ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎ-Π·ΠΎΠ½Π΄ΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠΈΠΊΡΠΎΠ°Π½Π°Π»ΠΈΠ·Π°, ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠ»ΡΠΎΡΠ΅ΡΡΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈ ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΠΈ ΡΠ΅Π·Π΅ΡΡΠΎΡΠ΄ΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΡΠ΅ΡΠ½ΠΈΡ. Π‘Π²ΠΎΠΉΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ°Π»ΠΈΠ·Π°ΡΠΎΡΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π²ΠΎΠ»ΡΡΠ°ΠΌΠΏΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΠΈ. ΠΠΎΠ»ΡΡΠ°Π΅ΠΌΡΠ΅ ΡΠ»Π΅ΠΊΡΡΠΎΠΊΠ°ΡΠ°Π»ΠΈΠ·Π°ΡΠΎΡΡ ΠΏΡΠΎΡΠ²Π»ΡΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ°Ρ
ΠΎΠΊΠΈΡΠ»Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠ°Π½ΠΎΠ»Π° ΠΈ ΡΡΠ°Π½ΠΎΠ»Π°.
Analysis of computational approaches for motif discovery
Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature
ΠΠ°ΡΠΈΡΠ½ΡΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π° Zr-ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ ΠΊΠΎΠ½Π²Π΅ΡΡΠΈΠΎΠ½Π½ΡΡ ΠΏΠΎΠΊΡΡΡΠΈΠΉ Π½Π° ΡΠΈΠ½ΠΊΠ΅
The aim of the study is to develop an environmentally friendly chromium-free passivation technology for galvanized steel. Passivation of zinc coatings was carried out by deposition of conversion coatings from solutions containing ZrO(NO3)2, Na2SiF6 and oxidizer H2O2 or K2S2O8. The effect of the solution pH, the concentration of Na2SiF6 and the type of oxidizer on the protective properties of coatings was studied by the drop method and electrochemical method of linear voltammetry in 3 % NaCl using the full factor experiment 23 . The main effects and effects of the interaction of the studied factors for the darkening time of the droplet and the dissolution potential of zinc are calculated. The solution pH in the presence of the oxidizing agent K2S2O8 influences the both parameters in the most extent. Concentration of Na2SiF6 has a significant effect on the dissolution potential of zinc and the least effect on the darkening time of the droplet. An increase in the solution pH and the concentration of Na2SiF6 increases the protective properties of the coatings. Measurements of the mass loss and open circuit potential during the resource testing of conversion coatings in 3% NaCl showed an increase in the corrosion rate over time.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΠΉ Π±Π΅ΡΡ
ΡΠΎΠΌΠΎΠ²ΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΏΠ°ΡΡΠΈΠ²Π°ΡΠΈΠΈ Π³Π°Π»ΡΠ²Π°Π½ΠΈΡΠ΅ΡΠΊΠΈ ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ. ΠΠ°ΡΡΠΈΠ²Π°ΡΠΈΡ Π³Π°Π»ΡΠ²Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ½ΠΊΠΎΠ²ΡΡ
ΠΏΠΎΠΊΡΡΡΠΈΠΉ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»Π°ΡΡ ΠΎΡΠ°ΠΆΠ΄Π΅Π½ΠΈΠ΅ΠΌ Π½Π° Π½ΠΈΡ
ΠΊΠΎΠ½Π²Π΅ΡΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΠΊΡΡΡΠΈΠΉ ΠΈΠ· ΡΠ°ΡΡΠ²ΠΎΡΠΎΠ², ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ZrO(NO3)2, Na2SiF6 ΠΈ ΠΎΠΊΠΈΡΠ»ΠΈΡΠ΅Π»Ρ H2O2 ΠΈΠ»ΠΈ K2S2O8. ΠΠ·ΡΡΠ°Π»ΠΎΡΡ Π²Π»ΠΈΡΠ½ΠΈΠ΅ pH ΡΠ°ΡΡΠ²ΠΎΡΠ°, ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Na2SiF6 ΠΈ ΡΠΈΠΏΠ° ΠΎΠΊΠΈΡΠ»ΠΈΡΠ΅Π»Ρ Π½Π° ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π·Π°ΡΠΈΡΠ½ΡΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΠΏΠΎΠΊΡΡΡΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΊΠ°ΠΏΠ»ΠΈ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ Π²ΠΎΠ»ΡΡΠ°ΠΌΠΏΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΠΈ Π² 3 %-Π½ΠΎΠΌ NaCl Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° 23 . Π Π°ΡΡΡΠΈΡΠ°Π½Ρ Π³Π»Π°Π²Π½ΡΠ΅ ΡΡΡΠ΅ΠΊΡΡ ΠΈ ΡΡΡΠ΅ΠΊΡΡ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π΄Π»Ρ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΏΠΎΡΠ΅ΠΌΠ½Π΅Π½ΠΈΡ ΠΊΠ°ΠΏΠ»ΠΈ ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΡΠ°ΡΡΠ²ΠΎΡΠ΅Π½ΠΈΡ ΡΠΈΠ½ΠΊΠ°. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΎΠ±Π° ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ pH ΡΠ°ΡΡΠ²ΠΎΡΠ° Π² ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΠΈ ΠΎΠΊΠΈΡΠ»ΠΈΡΠ΅Π»Ρ K2S2O8. ΠΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ Na2SiF6 ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π» ΡΠ°ΡΡΠ²ΠΎΡΠ΅Π½ΠΈΡ ΡΠΈΠ½ΠΊΠ° ΠΈ Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠ΅Π΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° Π²ΡΠ΅ΠΌΡ ΠΏΠΎΡΠ΅ΠΌΠ½Π΅Π½ΠΈΡ ΠΊΠ°ΠΏΠ»ΠΈ. Π£Π²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ pH ΡΠ°ΡΡΠ²ΠΎΡΠ° ΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Na2SiF6 ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°Π΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π·Π°ΡΠΈΡΠ½ΡΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΠΏΠΎΠΊΡΡΡΠΈΠΉ. ΠΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΠΈ ΠΌΠ°ΡΡΡ ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΡΠ°Π·ΠΎΠΌΠΊΠ½ΡΡΠΎΠΉ ΡΠ΅ΠΏΠΈ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ΅ΡΡΡΡΠ½ΡΡ
ΠΈΡΠΏΡΡΠ°Π½ΠΈΠΉ ΠΊΠΎΠ½Π²Π΅ΡΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΠΊΡΡΡΠΈΠΉ Π² 3 %-Π½ΠΎΠΌ NaCl ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π½ΠΈΠ΅ ΡΠΊΠΎΡΠΎΡΡΠΈ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ ΡΠΎ Π²ΡΠ΅ΠΌΠ΅Π½Π΅ΠΌ
ΠΠΎΡΡΠΎΠ·ΠΈΠΎΠ½Π½Π°Ρ ΡΡΠΎΠΉΠΊΠΎΡΡΡ Π³ΠΎΡΡΡΠ΅ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Π² Ρ Π»ΠΎΡΠΈΠ΄ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΡΡΠ΅Π΄Π΅
Today, corrosion and corrosion protection of metals are the most important scientific, technical, economic and environmental problems. The effect of additions of sodium molybdate, ammonium metavanadate, a mixture of sodium molybdate and ammonium metavanadate, thiourea and sodium orthophosphate on the corrosive behavior of hot-dip galvanized steel in a neutral and slightly alkaline chloride-containing medium has been studied. The experimental results obtained by weight and electrochemical methods proved sodium molybdate, ammonium metavanadate, a mixture of sodium molybdate and ammonium metavanadate, thiourea and sodium orthophosphate to be corrosion inhibitors that slow down the rate of destruction of hot-dip galvanized steel in a neutral and slightly alkaline chloride-containing medium by 1.5β11 times.ΠΠΎΡΡΠΎΠ·ΠΈΡ ΠΈ Π·Π°ΡΠΈΡΠ° ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠ² ΠΎΡ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ ΠΏΠΎ-ΠΏΡΠ΅ΠΆΠ½Π΅ΠΌΡ ΠΎΡΡΠ°ΡΡΡΡ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΠΌΠΈ Π½Π°ΡΡΠ½ΠΎ-ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ, ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π·Π°Π΄Π°ΡΠ°ΠΌΠΈ. Π ΡΠ°Π±ΠΎΡΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΎ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π΄ΠΎΠ±Π°Π²ΠΎΠΊ ΠΌΠΎΠ»ΠΈΠ±Π΄Π°ΡΠ° Π½Π°ΡΡΠΈΡ, ΠΌΠ΅ΡΠ°Π²Π°Π½Π°Π΄Π°ΡΠ° Π°ΠΌΠΌΠΎΠ½ΠΈΡ, ΡΠΌΠ΅ΡΠΈ ΠΌΠΎΠ»ΠΈΠ±Π΄Π°ΡΠ° Π½Π°ΡΡΠΈΡ ΠΈ ΠΌΠ΅ΡΠ°Π²Π°Π½Π°Π΄Π°ΡΠ° Π°ΠΌΠΌΠΎΠ½ΠΈΡ, ΡΠΈΠΎΠΌΠΎΡΠ΅Π²ΠΈΠ½Ρ, ΠΎΡΡΠΎΡΠΎΡΡΠ°ΡΠ° Π½Π°ΡΡΠΈΡ Π½Π° ΠΊΠΎΡΡΠΎΠ·ΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π³ΠΎΡΡΡΠ΅ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Π² Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ ΡΠ»Π°Π±ΠΎΡΠ΅Π»ΠΎΡΠ½ΠΎΠΉ Ρ
Π»ΠΎΡΠΈΠ΄ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΡΡΠ΅Π΄Π΅. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π²Π΅ΡΠΎΠ²ΡΠΌ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΌΠΎΠ»ΠΈΠ±Π΄Π°Ρ Π½Π°ΡΡΠΈΡ, ΠΌΠ΅ΡΠ°Π²Π°Π½Π°Π΄Π°Ρ Π°ΠΌΠΌΠΎΠ½ΠΈΡ, ΡΠΌΠ΅ΡΡ ΠΌΠΎΠ»ΠΈΠ±Π΄Π°ΡΠ° Π½Π°ΡΡΠΈΡ ΠΈ ΠΌΠ΅ΡΠ°Π²Π°Π½Π°Π΄Π°ΡΠ° Π°ΠΌΠΌΠΎΠ½ΠΈΡ, ΡΠΈΠΎΠΌΠΎΡΠ΅Π²ΠΈΠ½Π°, ΠΎΡΡΠΎΡΠΎΡΡΠ°Ρ Π½Π°ΡΡΠΈΡ ΠΏΡΠΎΡΠ²Π»ΡΡΡ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΡΡΡΠΈΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π°, ΡΠΌΠ΅Π½ΡΡΠ°Ρ ΡΠΊΠΎΡΠΎΡΡΡ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ Π³ΠΎΡΡΡΠ΅ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Π² Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ ΡΠ»Π°Π±ΠΎΡΠ΅Π»ΠΎΡΠ½ΠΎΠΉ Ρ
Π»ΠΎΡΠΈΠ΄ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΡΡΠ΅Π΄Π΅ Π² 1,5β11 ΡΠ°Π·
A Bayesian Search for Transcriptional Motifs
Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools
Statistical Modeling of Transcription Factor Binding Affinities Predicts Regulatory Interactions
Recent experimental and theoretical efforts have highlighted the fact that binding of transcription factors to DNA can be more accurately described by continuous measures of their binding affinities, rather than a discrete description in terms of binding sites. While the binding affinities can be predicted from a physical model, it is often desirable to know the distribution of binding affinities for specific sequence backgrounds. In this paper, we present a statistical approach to derive the exact distribution for sequence models with fixed GC content. We demonstrate that the affinity distribution of almost all known transcription factors can be effectively parametrized by a class of generalized extreme value distributions. Moreover, this parameterization also describes the affinity distribution for sequence backgrounds with variable GC content, such as human promoter sequences. Our approach is applicable to arbitrary sequences and all transcription factors with known binding preferences that can be described in terms of a motif matrix. The statistical treatment also provides a proper framework to directly compare transcription factors with very different affinity distributions. This is illustrated by our analysis of human promoters with known binding sites, for many of which we could identify the known regulators as those with the highest affinity. The combination of physical model and statistical normalization provides a quantitative measure which ranks transcription factors for a given sequence, and which can be compared directly with large-scale binding data. Its successful application to human promoter sequences serves as an encouraging example of how the method can be applied to other sequences
Transcriptional Autoregulatory Loops Are Highly Conserved in Vertebrate Evolution
BACKGROUND: Feedback loops are the simplest building blocks of transcriptional regulatory networks and therefore their behavior in the course of evolution is of prime interest. METHODOLOGY: We address the question of enrichment of the number of autoregulatory feedback loops in higher organisms. First, based on predicted autoregulatory binding sites we count the number of autoregulatory loops. We compare it to estimates obtained either by assuming that each (conserved) gene has the same chance to be a target of a given factor or by assuming that each conserved sequence position has an equal chance to be a binding site of the factor. CONCLUSIONS: We demonstrate that the numbers of putative autoregulatory loops conserved between human and fugu, danio or chicken are significantly higher than expected. Moreover we show, that conserved autoregulatory binding sites cluster close to the factors' starts of transcription. We conclude, that transcriptional autoregulatory feedback loops constitute a core transcriptional network motif and their conservation has been maintained in higher vertebrate organism evolution
A ChIP-Seq Benchmark Shows That Sequence Conservation Mainly Improves Detection of Strong Transcription Factor Binding Sites
Transcription factors are important controllers of gene expression and mapping transcription factor binding sites (TFBS) is key to inferring transcription factor regulatory networks. Several methods for predicting TFBS exist, but there are no standard genome-wide datasets on which to assess the performance of these prediction methods. Also, it is believed that information about sequence conservation across different genomes can generally improve accuracy of motif-based predictors, but it is not clear under what circumstances use of conservation is most beneficial.Here we use published ChIP-seq data and an improved peak detection method to create comprehensive benchmark datasets for prediction methods which use known descriptors or binding motifs to detect TFBS in genomic sequences. We use this benchmark to assess the performance of five different prediction methods and find that the methods that use information about sequence conservation generally perform better than simpler motif-scanning methods. The difference is greater on high-affinity peaks and when using short and information-poor motifs. However, if the motifs are specific and information-rich, we find that simple motif-scanning methods can perform better than conservation-based methods.Our benchmark provides a comprehensive test that can be used to rank the relative performance of transcription factor binding site prediction methods. Moreover, our results show that, contrary to previous reports, sequence conservation is better suited for predicting strong than weak transcription factor binding sites
ΠΠΠΠΠΠΠ’ΠΠ ΠΠΠ― ΠΠΠ©ΠΠ’Π ΠΠ¦ΠΠΠΠΠΠΠΠΠΠ Π‘Π’ΠΠΠ ΠΠΠΠΠΠΠ’ΠΠ ΠΠΠ’Π ΠΠ―
The results of investigation of corrosion inhibition of zinc-plated coatings in neutral chloride-containing corrosive medium by aqueous sodium vanadate solution are described. Investigations of corrosion inhibition of zinc-plated coatings on steel were performed by gravimetric and electrochemical method. The corrosive medium was neutral 3% sodium chloride solution, with a sodium vanadate concentration varied from 0.00005 M to 0.0003 M. Mass indices of corrosion, current density and corrosion potential of galvanized steel were determined depending on inhibitor concentration. Electrochemical studies show that the introduction of sodium vanadate in amounts of 0.00005β0.0003 M into the corrosive medium (3% sodium chloride solution) slows down the process of zinc corrosion. The corrosion process slows down by 3.3 times at an inhibitor concentration of 0.00005 M and by 20 times at an inhibitor concentration of 0.0002 M, respectively. An increase in the concentration of sodium vanadate to more than 0.0002 M is inappropriate, since an increase in the corrosion current occurs. The optimal corrosion inhibitor concentration for zinc-plated steel in 3% NaCl solution for Na3VO4 lies in the range of 0.0001β0.0002 Π. The protection effect of the inhibitor found by gravimetric and electrochemical methods equals to 40β76% and 93β95%, respectively.Β Β ΠΠΏΠΈΡΠ°Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΠΎΡΠΎΠ±Π° Π·Π°ΡΠΈΡΡ Π³Π°Π»ΡΠ²Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠ½ΠΊΠΎΠ²ΡΡ
ΠΏΠΎΠΊΡΡΡΠΈΠΉ Π² Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½ΠΎΠΉ Ρ
Π»ΠΎΡΠΈΠ΄ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠ΅ΠΉ ΡΡΠ΅Π΄Π΅ ΡΠ°ΡΡΠ²ΠΎΡΠΈΠΌΡΠΌ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠΎΠΌ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ Π²Π°Π½Π°Π΄Π°ΡΠΎΠΌ Π½Π°ΡΡΠΈΡ Na3VO4. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ Π³Π°Π»ΡΠ²Π°Π½ΠΈΡΠ΅ΡΠΊΠΈ ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Na3VO4 Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π²Π΅ΡΠΎΠ²ΡΠΌ ΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π² 3%-Π½ΠΎΠΌ ΡΠ°ΡΡΠ²ΠΎΡΠ΅ Ρ
Π»ΠΎΡΠΈΠ΄Π° Π½Π°ΡΡΠΈΡ Π² Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΉ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ° 0,0005β0,0003 Π. ΠΡΠ»ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΌΠ°ΡΡΠΎΠ²ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ, ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΡΠΎΠΊΠ° ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Ρ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ°. ΠΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ, ΡΡΠΎ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π² ΠΊΠΎΡΡΠΎΠ·ΠΈΠΎΠ½Π½ΡΡ ΡΡΠ΅Π΄Ρ (3% NaCl) Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ° Π²Π°Π½Π°Π΄Π°ΡΠ° Na3VO4 Π² ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π°Ρ
0,00005β0,0003 Π Π·Π°ΠΌΠ΅Π΄Π»ΡΠ΅Ρ ΠΏΡΠΎΡΠ΅ΡΡ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ ΡΠΈΠ½ΠΊΠ°. ΠΡΠΎΡΠ΅ΡΡ ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ Π·Π°ΠΌΠ΅Π΄Π»ΡΠ΅ΡΡΡ Π² 3,3 ΡΠ°Π·Π° ΠΏΡΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ° 0,00005 Π, ΠΈ Π² 20 ΡΠ°Π· ΠΏΡΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ° 0,0002 Π ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. Π£Π²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π²Π°Π½Π°Π΄Π°ΡΠ° Π½Π°ΡΡΠΈΡ Π±ΠΎΠ»Π΅Π΅ 0,0002 Π Π½Π΅ΡΠ΅Π»Π΅ΡΠΎΠΎΠ±ΡΠ°Π·Π½ΠΎ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΡΠΎΠΊΠ° ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ Π΄Π²ΡΡ
Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ½ΠΎΠΉ Π·Π°ΡΠΈΡΡ ΠΎΡΠΈΠ½ΠΊΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΡΠ°Π»ΠΈ Π²Π°Π½Π°Π΄Π°ΡΠΎΠΌ Na3VO4 ΠΌΠΎΠΆΠ½ΠΎ ΡΠ΄Π΅Π»Π°ΡΡ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½Π°Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ° ΠΊΠΎΡΡΠΎΠ·ΠΈΠΈ Na3VO4 Π² 3%-Π½ΠΎΠΌ ΡΠ°ΡΡΠ²ΠΎΡΠ΅ NaCl Π»Π΅ΠΆΠΈΡ Π² Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅ 0,0001β0,0002 Π. ΠΡΠΈ ΡΡΠΎΠΌ Π·Π°ΡΠΈΡΠ½ΡΠΉ ΡΡΡΠ΅ΠΊΡ ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΡΠ°, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠΉ Π²Π΅ΡΠΎΠ²ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ, ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 40β76%, Π° ΡΠ»Π΅ΠΊΡΡΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ β 93β95%.Β
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