40 research outputs found

    Molecular markers in the genetic diversity studies of representatives of the genus <i>Rubus</i> L. and prospects of their application in breeding

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    According to estimates of various taxonomists, the genus Rubus L. (Rosaceae Juss.) consists of 12-16 subgenera comprising ~750 species. The two largest subgenera are Idaeobatus (Focke) Focke, which includes raspberries, and the type subgenus Rubus (=Eubatus Focke), which contains blackberry species. Representatives of the genus Rubus have high nutritional and economic values, as well as medicinal properties. Breeding programs are aimed at broadening genetic diversity and creating new varieties of raspberries and blackberries that are resistant to biotic and abiotic stressors and have high fruit quality. Modern breeding and genetic programs increasingly use the achievements of molecular genetics and genomics. This paper reviews the literature data on the application of molecular markers in fundamental and applied research aimed at studying the genetic diversity of cultivated and wild species of the genus Rubus. The review describes the main types of molecular markers (RFLP, RAPD, SCoT, SSR, ISSR, AFLP, SCAR, SSCP) and their application for studying the species of the genus Rubus. The results of the work on the use of DNA markers for solving different tasks are presented, including: studying the phylogenetic relationships of species, clarifying controversial issues of taxonomy, analyzing interspecific and intraspecific diversity, genotyping and pedigree analysis of raspberry and blackberry varieties, studying somaclonal variation and others. The most important applied result is the development of molecular genetic maps for raspberry and blackberry species, on which numerous genes and QTLs conferring various valuable traits have been mapped. At the same time, the number of markers that are promising for effective molecular screening is still insufficient

    ΠŸΠ΅Ρ€ΠΈΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Π°Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° сывороточной ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ глиального фибриллярного кислого ΠΏΡ€ΠΎΡ‚Π΅ΠΈΠ½Π° ΠΈ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½ΠΎΠ΅ восстановлСниС: ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½ΠΎΠ΅ обсСрвационноС исслСдованиС

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    A number of studies have found an association between the increased concentration of glial fibrillar acid protein (GFAP) in blood serum in patients with various types of brain damage (ischemic stroke, traumatic brain injury, neurodegenerative and neuro-oncological diseases), as well as with a rapid decline in cognitive functions in elderly people with initially normal cognitive abilities.The objective: to identify the relationship between delayed cognitive recovery and changes in serum GFAP concentration in the perioperative period in patients operated for various oncological diseases.Subjects and Methods. The study included 30 patients who underwent surgical treatment for prostate cancer, colorectal cancer and pancreatic cancer under combined general anesthesia.The inclusion criteria were the expected duration of the operation over 300 minutes and the age over 60 years. GFAP was determined in plasma by enzyme immunoassay before anesthesia, the next day after surgery and on the 4th–5th day. Neuropsychological testing was performed before surgery and on the 4th–5th postoperative day. Delayed cognitive recovery was defined as a decrease in the composite z-score of more than one standard deviation (SD) compared to the preoperative assessment.Correlation analysis was performed between changes in the composite z-score (in absolute values) and the difference in GFAP concentration between the outcome and the first postoperative day, the outcome and the 4th–5th postoperative day and the first and 4th–5th postoperative days.Results. In five cases (16.6%), a decrease in the composite z-score &gt; 1 SD was revealed indicating a delayed cognitive recovery. In the remaining 25 (83.4%) patients, changes in the composite z-score were less than one standard deviation. The median concentration of GFAP in patients with delayed cognitive recovery was 0.13 [0.1; 0.14] before surgery, 0.12 [0.09; 0.14] the day after surgery and 0.16 [0.05; 0.19] on the 4th–5th day after surgery. In patients without cognitive impairment, the concentration of GFAP was 0.15 [0.125; 0.184] before surgery, 0.15 [0.121; 0.163] 24 hours after surgery and 0.13 [0.079; 0.151] on the 4th–5th day after surgery. The correlation values between changes in the composite z-score and the difference in GFAP concentrations were: between the outcome and the first postoperative day – rs = 0.107, p = 0.37, outcome and the 4th–5th postoperative day – rs = 0.134, p = 0.37, the first and 4thβ€’5th postoperative days – rs = 0.21, p = 0.37.Discussion. There was no statistically significant difference in GFAP levels between patients with delayed cognitive recovery and patients without cognitive impairment. There was also no correlation between the difference in GFAP concentrations in plasma before surgery and 24 hours after, before surgery and on the 4th–5th day of the postoperative period and the composite z-score.Conclusions. The use of GFAP to predict cognitive decline associated with surgical treatment of colorectal cancer, prostate cancer and pancreatic cancer under general anesthesia is not yet possible.Π’ рядС исслСдований Π±Ρ‹Π»Π° ΠΎΠ±Π½Π°Ρ€ΡƒΠΆΠ΅Π½Π° связь ΠΌΠ΅ΠΆΠ΄Ρƒ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½Π½ΠΎΠΉ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠ΅ΠΉ глиального фибриллярного кислого ΠΏΡ€ΠΎΡ‚Π΅ΠΈΠ½Π° (GFAP) Π² сывороткС ΠΊΡ€ΠΎΠ²ΠΈ Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹ΠΌΠΈ Π²Π°Ρ€ΠΈΠ°Π½Ρ‚Π°ΠΌΠΈ пораТСния Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° (ΠΈΡˆΠ΅ΠΌΠΈΡ‡Π΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡƒΠ»ΡŒΡ‚ΠΎΠΌ, травматичСским ΠΏΠΎΠ²Ρ€Π΅ΠΆΠ΄Π΅Π½ΠΈΠ΅ΠΌ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, Π½Π΅ΠΉΡ€ΠΎΠ΄Π΅Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΈΠ²Π½Ρ‹ΠΌΠΈ ΠΈ нСйроонкологичСскими заболСваниями), Π° Ρ‚Π°ΠΊΠΆΠ΅ с быстрым сниТСниСм ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹Ρ… Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ Ρƒ ΠΏΠΎΠΆΠΈΠ»Ρ‹Ρ… людСй c исходно Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹ΠΌΠΈ способностями.ЦСль: Π²Ρ‹ΡΠ²ΠΈΡ‚ΡŒ взаимосвязь ΠΌΠ΅ΠΆΠ΄Ρƒ Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½Ρ‹ΠΌ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹ΠΌ восстановлСниСм ΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ GFAP сыворотки ΠΊΡ€ΠΎΠ²ΠΈ Π² ΠΏΠ΅Ρ€ΠΈΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΌ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π΅ Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ², ΠΎΠΏΠ΅Ρ€ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΏΠΎ ΠΏΠΎΠ²ΠΎΠ΄Ρƒ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… онкологичСских Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π» ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. Π’ исслСдованиС Π²ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΎ 30 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ², ΠΏΠ΅Ρ€Π΅Π½Π΅ΡΡˆΠΈΡ… ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠ΅ Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ ΠΏΠΎ ΠΏΠΎΠ²ΠΎΠ΄Ρƒ Ρ€Π°ΠΊΠ° ΠΏΡ€Π΅Π΄ΡΡ‚Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ‹, ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ€Π°ΠΊΠ° ΠΈ Ρ€Π°ΠΊΠ° ΠΏΠΎΠ΄ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡Π½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ‹ Π² условиях ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΎΠ±Ρ‰Π΅ΠΉ анСстСзии. ΠšΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡΠΌΠΈ Π²ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΡ Π±Ρ‹Π»ΠΈ оТидаСмая ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ Π±ΠΎΠ»Π΅Π΅ 300 ΠΌΠΈΠ½ ΠΈ возраст Π±ΠΎΠ»Π΅Π΅ 60 Π»Π΅Ρ‚. GFAP опрСдСляли Π² ΠΏΠ»Π°Π·ΠΌΠ΅ ΠΈΠΌΠΌΡƒΠ½ΠΎΡ„Π΅Ρ€ΠΌΠ΅Π½Ρ‚Π½Ρ‹ΠΌ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ Π΄ΠΎ ввСдСния анСстСзии, Π½Π° ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ дСнь послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Π½Π° 4β€’5-Π΅ сут. НСйропсихологичСскоС тСстированиС выполняли Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Π½Π° 4β€’5-ΠΉ послСопСрационный дСнь. Π—Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½ΠΎΠ΅ восстановлСниС опрСдСляли ΠΊΠ°ΠΊ сниТСниС ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½ΠΎΠ³ΠΎ z-Π±Π°Π»Π»Π° Π±ΠΎΠ»Π΅Π΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ стандартного (SD) отклонСния ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ΠΏΡ€Π΅Π΄ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΎΠΉ. ΠšΠΎΡ€Ρ€Π΅Π»ΡΡ†ΠΈΠΎΠ½Π½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ измСнСниями ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½ΠΎΠ³ΠΎ z-Π±Π°Π»Π»Π° (Π² Π°Π±ΡΠΎΠ»ΡŽΡ‚Π½Ρ‹Ρ… значСниях) ΠΈ Ρ€Π°Π·Π½ΠΈΡ†Π΅ΠΉ Π² ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ GFAP ΠΌΠ΅ΠΆΠ΄Ρƒ исходом ΠΈ ΠΏΠ΅Ρ€Π²Ρ‹ΠΌ послСопСрационным Π΄Π½Π΅ΠΌ, исходом ΠΈ 4β€’5-ΠΌ послСопСрационным Π΄Π½Π΅ΠΌ ΠΈ ΠΏΠ΅Ρ€Π²Ρ‹ΠΌ ΠΈ 4β€’5-ΠΌ послСопСрационными днями.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π’ 5 (16,6%) случаях выявлСно сниТСниС ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½ΠΎΠ³ΠΎ z-Π±Π°Π»Π»Π° &gt; 1 SD, Ρ‡Ρ‚ΠΎ ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π»ΠΎ Π½Π° Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½ΠΎΠ΅ восстановлСниС. Π£ ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹Ρ… 25 (83,4%) ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² измСнСния ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½ΠΎΠ³ΠΎ z-Π±Π°Π»Π»Π° Π±Ρ‹Π»ΠΈ ΠΌΠ΅Π½Π΅Π΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ стандартного отклонСния. МСдиана ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ GFAP Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½Ρ‹ΠΌ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹ΠΌ восстановлСниСм составила 0,13 [0,1; 0,14] Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ, 0,12 [0,09; 0,14] Π½Π° ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ дСнь послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ 0,16 [0,05; 0,19] Π½Π° 4β€’5-Π΅ сут послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. Π£ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² Π±Π΅Π· ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹Ρ… Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΠΉ концСнтрация GFAP составила 0,15 [0,125; 0,184] Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ, 0,15 [0,121; 0,163] Ρ‡Π΅Ρ€Π΅Π· 24 послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ 0,13 [0,079; 0,151] Π½Π° 4β€’5-Π΅ сут послС ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. ЗначСния коррСляции ΠΌΠ΅ΠΆΠ΄Ρƒ измСнСниями ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½ΠΎΠ³ΠΎ z-Π±Π°Π»Π»Π° ΠΈ Ρ€Π°Π·Π½ΠΈΡ†Π΅ΠΉ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΉ GFAP составили: ΠΌΠ΅ΠΆΠ΄Ρƒ исходом ΠΈ ΠΏΠ΅Ρ€Π²Ρ‹ΠΌ послСопСрационным Π΄Π½Π΅ΠΌ – rs = 0,107, p = 0,37, исходом ΠΈ 4β€’5-ΠΌ послСопСрационным Π΄Π½Π΅ΠΌ – rs = 0,134, p = 0,37, ΠΏΠ΅Ρ€Π²Ρ‹ΠΌ ΠΈ 4β€’5-ΠΌ послСопСрационными днями – rs = 0,21, p = 0,37.ΠžΠ±ΡΡƒΠΆΠ΄Π΅Π½ΠΈΠ΅. НС выявлСно статистичСски Π·Π½Π°Ρ‡ΠΈΠΌΠΎΠΉ Ρ€Π°Π·Π½ΠΈΡ†Ρ‹ Π² уровнях GFAP ΠΌΠ΅ΠΆΠ΄Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°ΠΌΠΈ с Π·Π°ΠΌΠ΅Π΄Π»Π΅Π½Π½Ρ‹ΠΌ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹ΠΌ восстановлСниСм ΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°ΠΌΠΈ Π±Π΅Π· ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹Ρ… Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΠΉ. Π’Π°ΠΊΠΆΠ΅ Π½Π΅ ΠΎΠ±Π½Π°Ρ€ΡƒΠΆΠ΅Π½ΠΎ коррСляции ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ€Π°Π·Π½ΠΈΡ†Π΅ΠΉ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΉ GFAP Π² ΠΏΠ»Π°Π·ΠΌΠ΅ Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Ρ‡Π΅Ρ€Π΅Π· 24 Ρ‡ послС, Π΄ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Π½Π° 4β€’5-Π΅ сут послСопСрационного ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚Π½Ρ‹ΠΌ z-счСтом.Π’Ρ‹Π²ΠΎΠ΄Ρ‹. ИспользованиС GFAP для прогнозирования сниТСния ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹Ρ… Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ, связанного с ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½Ρ‹ΠΌ Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠ»ΠΎΡ€Π΅ΠΊΡ‚Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ€Π°ΠΊΠ°, Ρ€Π°ΠΊΠ° ΠΏΡ€Π΅Π΄ΡΡ‚Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ‹ ΠΈ Ρ€Π°ΠΊΠ° ΠΏΠΎΠ΄ΠΆΠ΅Π»ΡƒΠ΄ΠΎΡ‡Π½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ‹ Π² условиях ΠΎΠ±Ρ‰Π΅ΠΉ анСстСзии, ΠΏΠΎΠΊΠ° Π½Π΅ прСдставляСтся Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹ΠΌ

    Biochemical applications of surface-enhanced infrared absorption spectroscopy

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    An overview is presented on the application of surface-enhanced infrared absorption (SEIRA) spectroscopy to biochemical problems. Use of SEIRA results in high surface sensitivity by enhancing the signal of the adsorbed molecule by approximately two orders of magnitude and has the potential to enable new studies, from fundamental aspects to applied sciences. This report surveys studies of DNA and nucleic acid adsorption to gold surfaces, development of immunoassays, electron transfer between metal electrodes and proteins, and protein–protein interactions. Because signal enhancement in SEIRA uses surface properties of the nano-structured metal, the biomaterial must be tethered to the metal without hampering its functionality. Because many biochemical reactions proceed vectorially, their functionality depends on proper orientation of the biomaterial. Thus, surface-modification techniques are addressed that enable control of the proper orientation of proteins on the metal surface. [Figure: see text

    Hydrothermal Synthesis of Delafossite-Type Oxides

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    The syntheses of copper and silver delafossite-type oxides from their constituent binary metal oxides, oxide hydroxides and hydroxides, by low temperature (<210 Β°C) and low pressure (<20 atm) hydrothermal reactions are described. Particular emphasis is placed on how the acid-base character of a constituent oxide determines its solubility and therefore whether a particular delafossite-type oxide can be synthesized, a strategy utilized by geologists and mineralogists to understand the conditions necessary for the synthesis of various minerals. Thus, the geochemical and corrosion science literature are shown to be useful in understanding the reaction conditions required for the syntheses of delafossite-type oxides and the relationship between reactant metal oxide acid-base character, solubility, aqueous speciation, and product formation. Manipulation of the key parameters, temperature, pressure, pH, and reactant solubility, results in broad families of phase-pure delafossite-type oxides in moderate to high yields for copper, CuBO2 (B) Al, Sc, Cr, Mn, Fe, Co, Ga, and Rh), and silver, AgBO2 (B ) Al, Sc, Fe, Co, Ni, Ga, Rh, In, and Tl)

    ANALYSIS OF THE THREE-DIMENSIONAL VECTOR FAÇADE MODEL CREATED FROM PHOTOGRAMMETRIC DATA

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    The results of the accuracy assessment analysis for creation of a three-dimensional vector model of building façade are described. In the framework of the analysis, analytical comparison of three-dimensional vector façade models created by photogrammetric and terrestrial laser scanning data has been done. The three-dimensional model built from TLS point clouds was taken as the reference one. In the course of the experiment, the three-dimensional model to be analyzed was superimposed on the reference one, the coordinates were measured and deviations between the same model points were determined. The accuracy estimation of the three-dimensional model obtained by using non-metric digital camera images was carried out. Identified façade surface areas with the maximum deviations were revealed
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