Differential response to resistance training in CHF according to ACE genotype

The Angiotensin Converting Enzyme (ACE) gene may influence the risk of heart disease and the response to various forms of exercise training may be at least partly dependent on the ACE genotype. We aimed to determine the effect of ACE genotype on the response to moderate intensity circuit resistance training in chronic heart failure (CHF) patients. Methods: The relationship between ACE genotype and the response to 11 weeks of resistance exercise training was determined in 37 CHF patients (New York Heart Association Functional Class=2.3±0.5; left ventricular ejection fraction 28±7%; age 64±12 years; 32:5 male:female) who were randomised to either resistance exercise (n=19) or inactive control group (n=18). Outcome measures included V˙ O2peak, peak power output and muscle strength and endurance. ACE genotype was determined using standard methods. Results: At baseline, patients who were homozygous for the I allele had higher V˙ O2peak (p=0.02) and peak power (p=0.003) compared to patients who were homozygous for the D allele. Patients with the D allele, who were randomised to resistance training, compared to non-exercising controls, had greater peak power increases (ID pb0.001; DD pb0.001) when compared with patients homozygous for the I allele, who did not improve. No significant genotype-dependent changes were observed in V˙ O2peak, muscle strength, muscle endurance or lactate threshold. Conclusion: ACE genotype may have a role in exercise tolerance in CHF and could also influence the effectiveness of resistance training in this condition

QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.Horticulture and Landscape Architectur

Association of ACTN3 R577X Polymorphism with Elite Basketball Player Status and Training Responses

The α-actinin-3 (ACTN3) gene rs1815739 (C/T, R577X) polymorphism is a variant frequently associated with athletic performance among different populations. However, there is limited research on the impact of this variant on athlete status and physical performance in basketball players. Therefore, the aim of this study was twofold: (1) to determine the association of ACTN3 rs1815739 polymorphism with changes in physical performance in response to six weeks of training in elite basketball players using 30 m sprint and Yo-Yo Intermittent Recovery Test Level 2 (IR 2) tests, and (2) to compare ACTN3 genotype and allelic frequencies between elite basketball players and controls. The study included a total of 363 individuals, comprising 101 elite basketball players and 262 sedentary individuals. Genomic DNA was isolated from oral epithelial cells or leukocytes, and genotyping was performed by real-time PCR using KASP genotyping method or by microarray analysis. We found that the frequency of the ACTN3 rs1815739 XX genotype was significantly lower in basketball players compared to controls (10.9 vs. 21.4%, p = 0.023), suggesting that RR/RX genotypes were more favorable for playing basketball. Statistically significant (p = 0.045) changes were observed in Yo-Yo IRT 2 performance measurement tests in basketball players with the RR genotype only. In conclusion, our findings suggest that the carriage of the ACTN3 rs1815739 R allele may confer an advantage in basketball

Deep learning based mask detection in smart home entries during the epidemic process

5th International Conference on Smart City Applications, SCA 2020 -- 7 October 2020 through 8 October 2020 -- Safranbolu -- 167323In this study, "smart home" systems were designed against Covid-19 virus, which negatively affects life all over the world, and viruses that may become epidemics later. Our homes need to be more hygienic and safe than yesterday. One of these hygiene rules is the masks that cover our nose and mouth. It is very important to use a mask to prevent further spread of the virus. Whether or not the people in smart homes are wearing masks at home will be diagnosed with the deep learning method. Hosts will be warned if they do not have masks. Brightness level control card and illuminator have been added to smart home entrances to better identify people's faces. With PID, the illumination level is fixed at the desired value, and with IOT technology, people can follow the illumination level at the smart home entrance from the mobile application. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved

DEEP LEARNING BASED MASK DETECTION IN SMART HOME ENTRIES DURING THE EPIDEMIC PROCESS

Abstract. In this study, "smart home" systems were designed against Covid-19 virus, which negatively affects life all over the world, and viruses that may become epidemics later. Our homes need to be more hygienic and safe than yesterday. One of these hygiene rules is the masks that cover our nose and mouth. It is very important to use a mask to prevent further spread of the virus. Whether or not the people in smart homes are wearing masks at home will be diagnosed with the deep learning method. Hosts will be warned if they do not have masks. Brightness level control card and illuminator have been added to smart home entrances to better identify people's faces. With PID, the illumination level is fixed at the desired value, and with IOT technology, people can follow the illumination level at the smart home entrance from the mobile application. </jats:p

Stratigraphy of the Bolu Massif (north of Bolu) and its close vicinity [Bolu Masifi (Bolu kuzeyi) ve yakin civarinin stratigrafisi]

The study area covers the Bolu Massif and its surroundings which are located at the north of the Bolu. In the study area, thirteen units have been discriminated as Karadere metamorphites (Precambrian), Yellice formation (Cambrian-Ordovician), Dirgine Granitoidleri (Ordovician), Isigandere formation (Ordovician-Silurian), Kocadere formation (Silurian-Early Devonian), Haciyerdere formation (Middle Devonian), Hizardere formation (Late Cretaceous), Sarikaya formation (Late Cretaceous-Paleocene), Melendere formation (Early Eocene), Cukurca formation (Middle Eocene), Keltepe Volcanites (Neogene), Orencik formation (Plio-Quaternary), Quaternary alluvium (Quaternary). During Early Paleozoic, volcanic and granitic rocks have been placed in the area. Between Ordovician-Middle Devonian, the succession which occurred initialy as clastic and then carbonate sedimentation has been interrupted from Mid-Devonian to Late Cretaceous. During the time span between Cretaceous and Quaternary, the sedimentation of epiclastic and carbonate rocks has taken place accompanying with a volcanic activity

Bolu Masifi (Bolu kuzeyi) ve yakin civarinin stratigrafisi

The study area covers the Bolu Massif and its surroundings which are located at the north of the Bolu. In the study area, thirteen units have been discriminated as Karadere metamorphites (Precambrian), Yellice formation (Cambrian-Ordovician), Dirgine Granitoidleri (Ordovician), Isigandere formation (Ordovician-Silurian), Kocadere formation (Silurian-Early Devonian), Haciyerdere formation (Middle Devonian), Hizardere formation (Late Cretaceous), Sarikaya formation (Late Cretaceous-Paleocene), Melendere formation (Early Eocene), Cukurca formation (Middle Eocene), Keltepe Volcanites (Neogene), Orencik formation (Plio-Quaternary), Quaternary alluvium (Quaternary). During Early Paleozoic, volcanic and granitic rocks have been placed in the area. Between Ordovician-Middle Devonian, the succession which occurred initialy as clastic and then carbonate sedimentation has been interrupted from Mid-Devonian to Late Cretaceous. During the time span between Cretaceous and Quaternary, the sedimentation of epiclastic and carbonate rocks has taken place accompanying with a volcanic activity