328 research outputs found
Π‘ΠΈΠ½ΡΠ΅Π· ΡΠ° Π°Π½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½Π° Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ 1-Π°Π»ΠΊΡΠ»-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΡΠΎΠ½ΡΠ²
An effective approach for synthesis of 5-methyl-3-phenyl-6-(5-phenyl-1,3,4-oxadiazol-2-yl)thieno[2,3-d]pyrimidine-2,4(1H,3H)-dione by 1,1β-carbonyldiimidazole promoted interaction of 5-methyl-2,4-dioxo-3-phenyl-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidine-6-carboxylic acid with benzohydrazide has been developed. The procedure also includes cyclization of Nβ-benzoyl-5-methyl-2,4-dioxo-3-phenyl-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidine-6-carbohydrazide obtained by boiling in phosphorous oxychloride and further hydrolysis of the chlorine atom at position 2 of the thieno[2,3-d]pyrimidine system. Alkylation of the assembly of two heterocyclic units obtained with benzyl chlorides, chloroacetamides, and 5-(chloromethyl)-3-aryl-1,2,4-oxadiazoles has allowed obtaining of 1-alkyl-5-methyl-3-phenyl-6-(5-phenyl-1,3,4-oxadiazol-2-yl)thieno[2,3-d]pyrimidine-2,4(1H,3H)-diones. The structures of the compounds obtained have been confirmed by the 1H NMR, chromato-mass spectral and elemental microanalysis data. The results of the screening performed by the agar diffusion method (βwell methodβ) have shown the absence of the antimicrobial activity for 1-benzyl-5-methyl-3-phenyl-6-(5-phenyl-1,3,4-oxadiazol-2-yl)thieno[2,3-d]pyrimidine-2,4(1H,3H)-diones and 2-[5-methyl-2,4-dioxo-3-phenyl-6-(5-phenyl-1,3,4-oxadiazol-2-yl)-3,4-dihydrothieno[2,3-d]pyrimidin-1(2H)-yl]-N-arylacetamides; but the activity for 1-{[3-aryl-1,2,4-oxadiazol-5-yl]methyl}-5-methyl-3-phenyl-6-(5-phenyl-1,3,4-oxadiazol-2-yl)thieno[2,3-d]pyrimidine-2,4(1H,3H)-diones has been found. The compounds of this range appeared to be active against the strains of Staphylococcus aureus, Escherichia coli and Bacillus subtilis; the diameters of their growth inhibition zones were similar to those for the reference drugs Metronidazole and Streptomycin.Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠΈΠ½ΡΠ΅Π·Ρ 5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΠΈΠ»-6-(5-ΡΠ΅Π½ΠΈΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-2-ΠΈΠ»)ΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΠΈΠΎΠ½Π° ΠΏΡΡΠ΅ΠΌ ΠΏΡΠΎΠΌΠΎΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ 1,1β-ΠΊΠ°ΡΠ±ΠΎΠ½ΠΈΠ»Π΄ΠΈΠΈΠΌΠΈΠ΄Π°Π·ΠΎΠ»ΠΎΠΌ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ 5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΠΈΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΠΈΠ»-1,2,3,4-ΡΠ΅ΡΡΠ°Π³ΠΈΠ΄ΡΠΎΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-6-ΠΊΠ°ΡΠ±ΠΎΠ½ΠΎΠ²ΠΎΠΉ ΠΊΠΈΡΠ»ΠΎΡΡ Ρ Π±Π΅Π½Π·ΠΎΠ³ΠΈΠ΄ΡΠ°Π·ΠΈΠ΄ΠΎΠΌ. ΠΡΠΎΡΠ΅Π΄ΡΡΠ° ΡΠ°ΠΊΠΆΠ΅ Π²ΠΊΠ»ΡΡΠ°Π΅Ρ ΡΠΈΠΊΠ»ΠΈΠ·Π°ΡΠΈΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠ³ΠΎ Nβ-Π±Π΅Π½Π·ΠΎΠΈΠ»-5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΠΈΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΠΈΠ»-1,2,3,4-ΡΠ΅ΡΡΠ°Π³ΠΈΠ΄ΡΠΎΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-6-ΠΊΠ°ΡΠ±ΠΎΠ³ΠΈΠ΄ΡΠ°Π·ΠΈΠ΄Π° ΠΊΠΈΠΏΡΡΠ΅Π½ΠΈΠ΅ΠΌ Π² Ρ
Π»ΠΎΡΠΎΠΊΠΈΡΠΈ ΡΠΎΡΡΠΎΡΠ° ΠΈ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΠΉ Π³ΠΈΠ΄ΡΠΎΠ»ΠΈΠ· Π°ΡΠΎΠΌΠ° Ρ
Π»ΠΎΡΠ° Π² ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠΈ 2 ΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½ΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΠ»ΠΊΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠ³ΠΎ Π΄Π²ΡΡ
Π·Π²Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½ΡΠ°ΠΌΠ±Π»Ρ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΎΠ² Π±Π΅Π½Π·ΠΈΠ»Ρ
Π»ΠΎΡΠΈΠ΄Π°ΠΌΠΈ, Ρ
Π»ΠΎΡΠ°ΡΠ΅ΡΠ°ΠΌΠΈΠ΄Π°ΠΌΠΈ ΠΈ 5-(Ρ
Π»ΠΎΡΠΌΠ΅ΡΠΈΠ»)-3-Π°ΡΠΈΠ»-1,2,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»Π°ΠΌΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΏΠΎΠ»ΡΡΠΈΡΡ 1-Π°Π»ΠΊΠΈΠ»-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΠΈΠ»-6-(5-ΡΠ΅Π½ΠΈΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-2-ΠΈΠ»)ΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΠΈΠΎΠ½Ρ. Π‘ΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ Π±ΡΠ»ΠΈ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
1Π Π―ΠΠ , Ρ
ΡΠΎΠΌΠ°ΡΠΎΠΌΠ°Ρ ΡΠΏΠ΅ΠΊΡΡΠΎΠ² ΠΈ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΡΠΊΡΠΈΠ½ΠΈΠ½Π³Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄ΠΈΡΡΡΠ·ΠΈΠΈ Π² Π°Π³Π°Ρ (Β«ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠΎΠ»ΠΎΠ΄ΡΠ΅Π²Β») ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΌΠΈΠΊΡΠΎΠ±Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Ρ 1-Π±Π΅Π½Π·ΠΈΠ»-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΠΈΠ»-6-(5-ΡΠ΅Π½ΠΈΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-2-ΠΈΠ»)ΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΠΈΠΎΠ½ΠΎΠ² ΠΈ 2-[5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΠΈΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΠΈΠ»-6-(5-ΡΠ΅Π½ΠΈΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-2-ΠΈΠ»)-3,4-Π΄ΠΈΠ³ΠΈΠ΄ΡΠΎΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-1(2H)-ΠΈΠ»]-N-Π°ΡΠΈΠ»Π°ΡΠ΅ΡΠ°ΠΌΠΈΠ΄ΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΌΠΈΠΊΡΠΎΠ±Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π΄Π»Ρ 1-{[3-Π°ΡΠΈΠ»-1,2,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-5-ΠΈΠ»]ΠΌΠ΅ΡΠΈΠ»}-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΠΈΠ»-6-(5-ΡΠ΅Π½ΠΈΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΠΈΠ°Π·ΠΎΠ»-2-ΠΈΠ»)ΡΠΈΠ΅Π½ΠΎ[2,3-d]ΠΏΠΈΡΠΈΠΌΠΈΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΠΈΠΎΠ½ΠΎΠ². ΠΠ°Π½Π½ΡΠ΅ Π²Π΅ΡΠ΅ΡΡΠ²Π° ΠΏΡΠΎΡΠ²ΠΈΠ»ΠΈ ΠΏΡΠΎΡΠΈΠ²ΠΎΠΌΠΈΠΊΡΠΎΠ±Π½ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΒ ΠΊ ΡΡΠ°ΠΌΠΌΠ°ΠΌ Staphylococcus aureus, Escherichia coli ΠΈ BaΡillus subtilis ΡΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡΠΌΠΈ Π·ΠΎΠ½ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ ΡΠΎΡΡΠ°, Π±Π»ΠΈΠ·ΠΊΠΈΠΌΠΈ ΠΊ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠ°ΠΌ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΡΠΎΠ½ΠΈΠ΄Π°Π·ΠΎΠ»Ρ ΠΈ ΡΡΡΠ΅ΠΏΡΠΎΠΌΠΈΡΠΈΠ½Ρ.Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΎ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎ ΡΠΈΠ½ΡΠ΅Π·Ρ 5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΡΠΎΠ½Ρ ΡΠ»ΡΡ
ΠΎΠΌ ΠΏΡΠΎΠΌΠΎΡΠΎΠ²Π°Π½ΠΎΡ 1,1β-ΠΊΠ°ΡΠ±ΠΎΠ½ΡΠ»Π΄ΡΡΠΌΡΠ΄Π°Π·ΠΎΠ»ΠΎΠΌ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ 5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΡΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΡΠ»-1,2,3,4-ΡΠ΅ΡΡΠ°Π³ΡΠ΄ΡΠΎΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-6-ΠΊΠ°ΡΠ±ΠΎΠ½ΠΎΠ²ΠΎΡ ΠΊΠΈΡΠ»ΠΎΡΠΈ Π· Π±Π΅Π½Π·ΠΎΠ³ΡΠ΄ΡΠ°Π·ΠΈΠ΄ΠΎΠΌ. ΠΡΠΎΡΠ΅Π΄ΡΡΠ° ΡΠ°ΠΊΠΎΠΆ Π²ΠΊΠ»ΡΡΠ°Ρ Π½Π°ΡΡΡΠΏΠ½Ρ ΡΠΈΠΊΠ»ΡΠ·Π°ΡΡΡ ΠΎΡΡΠΈΠΌΠ°Π½ΠΎΠ³ΠΎ Nβ-Π±Π΅Π½Π·ΠΎΡΠ»-5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΡΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΡΠ»-1,2,3,4-ΡΠ΅ΡΡΠ°-Π³ΡΠ΄ΡΠΎΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-6-ΠΊΠ°ΡΠ±ΠΎΠ³ΡΠ΄ΡΠ°Π·ΠΈΠ΄Ρ ΠΊΠΈΠΏβΡΡΡΠ½Π½ΡΠΌ Ρ Ρ
Π»ΠΎΡΠΎΠΊΠΈΡΡ ΡΠΎΡΡΠΎΡΡ ΡΠ° ΠΏΠΎΠ΄Π°Π»ΡΡΠΈΠΉ Π³ΡΠ΄ΡΠΎΠ»ΡΠ·Β Π°ΡΠΎΠΌΠ° Ρ
Π»ΠΎΡΡ Ρ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½Ρ 2 ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½ΠΎΠ²ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ. ΠΠ»ΠΊΡΠ»ΡΠ²Π°Π½Π½Ρ ΠΎΡΡΠΈΠΌΠ°Π½ΠΎΠ³ΠΎ Π΄Π²ΠΎΠ»Π°Π½ΠΊΠΎΠ²ΠΎΠ³ΠΎ Π°Π½ΡΠ°ΠΌΠ±Π»Ρ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΡΠ² Π±Π΅Π½Π·ΠΈΠ»Ρ
Π»ΠΎΡΠΈΠ΄Π°ΠΌΠΈ, Ρ
Π»ΠΎΡΠΎΠ°ΡΠ΅ΡΠ°ΠΌΡΠ΄Π°ΠΌΠΈ ΡΠ° 5-(Ρ
Π»ΠΎΡΠΎΠΌΠ΅ΡΠΈΠ»)-3-Π°ΡΠΈΠ»-1,2,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎ-Π»Π°ΠΌΠΈ Π΄ΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΎΡΡΠΈΠΌΠ°ΡΠΈ 1-Π°Π»ΠΊΡΠ»-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡ-Π΄ΠΈΠ½-2,4(1H,3H)-Π΄ΡΠΎΠ½ΠΈ. Π‘ΡΡΡΠΊΡΡΡΠΈ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π±ΡΠ»ΠΈ ΠΏΡΠ΄ΡΠ²Π΅ΡΠ΄ΠΆΠ΅Π½Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π΄Π°Π½ΠΈΡ
1Π Π―ΠΠ , Ρ
ΡΠΎΠΌΠ°ΡΠΎΠΌΠ°Ρ ΡΠΏΠ΅ΠΊΡΡΡΠ² ΡΠ° Π΅Π»Π΅ΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΡΠ·Ρ. ΠΠ° ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΡΠΊΡΠΈΠ½ΡΠ½Π³Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π΄ΠΈΡΡΠ·ΡΡ Π² Π°Π³Π°Ρ (Β«ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠΎΠ»ΠΎΠ΄ΡΠ·ΡΠ²Β») Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ Π²ΡΠ΄ΡΡΡΠ½ΡΡΡΡ Π°Π½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½ΠΎΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Ρ 1-Π±Π΅Π½Π·ΠΈΠ»-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΡΠΎΠ½ΡΠ² ΡΠ° 2-[5-ΠΌΠ΅ΡΠΈΠ»-2,4-Π΄ΡΠΎΠΊΡΠΎ-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)-3,4-Π΄ΠΈΠ³ΡΠ΄ΡΠΎΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-1(2H)-ΡΠ»]-N-Π°ΡΠΈΠ»Π°ΡΠ΅ΡΠ°ΠΌΡΠ΄ΡΠ², Π° ΡΠ°ΠΊΠΎΠΆ Π½Π°ΡΠ²Π½ΡΡΡΡ Π°Π½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½ΠΎΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π΄Π»Ρ 1-{[3-Π°ΡΠΈΠ»-1,2,4-ΠΎΠΊΡΠ°Π΄ΡΠ°Π·ΠΎΠ»-5-ΡΠ»]ΠΌΠ΅ΡΠΈΠ»}-5-ΠΌΠ΅ΡΠΈΠ»-3-ΡΠ΅Π½ΡΠ»-6-(5-ΡΠ΅Π½ΡΠ»-1,3,4-ΠΎΠΊΡΠ°-Π΄ΡΠ°Π·ΠΎΠ»-2-ΡΠ»)ΡΡΡΠ½ΠΎ[2,3-d]ΠΏΡΡΠΈΠΌΡΠ΄ΠΈΠ½-2,4(1H,3H)-Π΄ΡΠΎΠ½ΡΠ². ΠΠ°Π½Ρ ΡΠ΅ΡΠΎΠ²ΠΈΠ½ΠΈ Π²ΠΈΡΠ²ΠΈΠ»ΠΈ Π°Π½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½Ρ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ Π΄ΠΎ ΡΡΠ°ΠΌΡΠ² Staphylococcus aureus, Escherichia coli ΡΠ° BaΡillus subtilis ΡΠ· Π·Π½Π°ΡΠ΅Π½Π½ΡΠΌΠΈ Π·ΠΎΠ½ Π·Π°ΡΡΠΈΠΌΠΊΠΈ ΡΠΎΡΡΡ, Π±Π»ΠΈΠ·ΡΠΊΠΈΠΌΠΈ Π΄ΠΎ ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΡΠ² ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ ΠΌΠ΅ΡΡΠΎΠ½ΡΠ΄Π°Π·ΠΎΠ»Ρ ΡΠ° ΡΡΡΠ΅ΠΏΡΠΎΠΌΡΡΠΈΠ½Ρ
Pair decay width of the Hoyle state and carbon production in stars
Electron scattering off the first excited 0+ state in 12C (the Hoyle state)
has been performed at low momentum transfers at the S-DALINAC. The new data
together with a novel model-independent analysis of the world data set covering
a wide momentum transfer range result in a highly improved transition charge
density from which a pair decay width Gamma_pi = (62.3 +- 2.0) micro-eV of the
Hoyle state was extracted reducing the uncertainty of the literature values by
more than a factor of three. A precise knowledge of Gamma_pi is mandatory for
quantitative studies of some key issues in the modeling of supernovae and of
asymptotic giant branch stars, the most likely site of the slow-neutron
nucleosynthesis process.Comment: 4 pages, 4 figures, accepted for publication in Phys. Rev. Let
How quantum bound states bounce and the structure it reveals
We investigate how quantum bound states bounce from a hard surface. Our
analysis has applications to ab initio calculations of nuclear structure and
elastic deformation, energy levels of excitons in semiconductor quantum dots
and wells, and cold atomic few-body systems on optical lattices with sharp
boundaries. We develop the general theory of elastic reflection for a composite
body from a hard wall. On the numerical side we present ab initio calculations
for the compression of alpha particles and universal results for two-body
states. On the analytical side we derive a universal effective potential that
gives the reflection scattering length for shallow two-body states.Comment: final publication version, new lattice results on alpha particle
compression, 5 pages, 2 figure
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which
measures the radio emission of the cosmic-ray air-showers in the frequency band
of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and
consists of 63 antennas, 57 of them are in a densely instrumented area of about
1 km\textsuperscript{2}. In the present work we discuss the improvements of the
signal reconstruction applied for the Tunka-Rex. At the first stage we
implemented matched filtering using averaged signals as template. The
simulation study has shown that matched filtering allows one to decrease the
threshold of signal detection and increase its purity. However, the maximum
performance of matched filtering is achievable only in case of white noise,
while in reality the noise is not fully random due to different reasons. To
recognize hidden features of the noise and treat them, we decided to use
convolutional neural network with autoencoder architecture. Taking the recorded
trace as an input, the autoencoder returns denoised trace, i.e. removes all
signal-unrelated amplitudes. We present the comparison between standard method
of signal reconstruction, matched filtering and autoencoder, and discuss the
prospects of application of neural networks for lowering the threshold of
digital antenna arrays for cosmic-ray detection.Comment: ARENA2018 proceeding
Current Status and New Challenges of The Tunka Radio Extension
The Tunka Radio Extension (Tunka-Rex) is an antenna array spread over an area
of about 1~km. The array is placed at the Tunka Advanced Instrument for
cosmic rays and Gamma Astronomy (TAIGA) and detects the radio emission of air
showers in the band of 30 to 80~MHz. During the last years it was shown that a
sparse array such as Tunka-Rex is capable of reconstructing the parameters of
the primary particle as accurate as the modern instruments. Based on these
results we continue developing our data analysis. Our next goal is the
reconstruction of cosmic-ray energy spectrum observed only by a radio
instrument. Taking a step towards it, we develop a model of aperture of our
instrument and test it against hybrid TAIGA observations and Monte-Carlo
simulations. In the present work we give an overview of the current status and
results for the last five years of operation of Tunka-Rex and discuss prospects
of the cosmic-ray energy estimation with sparse radio arrays.Comment: Proceedings of E+CRS 201
Improved measurements of the energy and shower maximum of cosmic rays with Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is an array of 63 antennas located in
the Tunka Valley, Siberia. It detects radio pulses in the 30-80 MHz band
produced during the air-shower development. As shown by Tunka-Rex, a sparse
radio array with about 200 m spacing is able to reconstruct the energy and the
depth of the shower maximum with satisfactory precision using simple methods
based on parameters of the lateral distribution of amplitudes. The LOFAR
experiment has shown that a sophisticated treatment of all individually
measured amplitudes of a dense antenna array can make the precision comparable
with the resolution of existing optical techniques. We develop these ideas
further and present a method based on the treatment of time series of measured
signals, i.e. each antenna station provides several points (trace) instead of a
single one (amplitude or power). We use the measured shower axis and energy as
input for CoREAS simulations: for each measured event we simulate a set of
air-showers with proton, helium, nitrogen and iron as primary particle (each
primary is simulated about ten times to cover fluctuations in the shower
maximum due to the first interaction). Simulated radio pulses are processed
with the Tunka-Rex detector response and convoluted with the measured signals.
A likelihood fit determines how well the simulated event fits to the measured
one. The positions of the shower maxima are defined from the distribution of
chi-square values of these fits. When using this improved method instead of the
standard one, firstly, the shower maximum of more events can be reconstructed,
secondly, the resolution is increased. The performance of the method is
demonstrated on the data acquired by the Tunka-Rex detector in 2012-2014.Comment: Proceedings of the 35th ICRC 2017, Busan, Kore
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