scholarly journals Role of the hull in the ripening of rice plant. IX. Physiological characteristics of hulls of unclosed and wounded spikelets, and relationship between hull and kernel development.

1987 ◽  
Vol 56 (3) ◽  
pp. 363-366
Author(s):  
Seok Weon SEO ◽  
Yasuo OTA
2021 ◽  
Author(s):  
Shumao Cui ◽  
Jie Jiang ◽  
Bowen Li ◽  
R. Paul Ross ◽  
Catherine Stanton ◽  
...  

The role of Pediococcus pentosaceus in gastrointestinne has received considerable attention in recent decades. This study aimed to investigate the effects of short-term administration of P. pentosaceus on physiological characteristics,...


2019 ◽  
Vol 38 (4) ◽  
pp. 1158-1167 ◽  
Author(s):  
Gayatri Gouda ◽  
Manoj Kumar Gupta ◽  
Ravindra Donde ◽  
Jitendra Kumar ◽  
Ramakrishna Vadde ◽  
...  

2013 ◽  
Vol 59 (4) ◽  
pp. 548-558 ◽  
Author(s):  
Yong Li ◽  
Takeshi Watanabe ◽  
Jun Murase ◽  
Susumu Asakawa ◽  
Makoto Kimura
Keyword(s):  

Author(s):  
Fenghao Zhang ◽  
Jie Dai ◽  
Tingtao Chen

Infertility has become a common problem in recent decades. The pathogenesis of infertility is variable, but microbiological factors account for a large proportion of it. Dysbiosis of vaginal microbiota is reportedly associated with female infertility, but the influence of normal vaginal microbiota on infertility is unclear. In this review, we summarize the physiological characteristics of the vaginal tract and vaginal microbiota communities. We mainly focus on the bacterial adherence of vaginal Lactobacillus species. Given that the adherent effect plays a crucial role in the colonization of bacteria, we hypothesize that the adherent effect of vaginal Lactobacillus may also influence the fertility of the host. We also analyze the agglutination and immobilization effects of other bacteria, especially Escherichia coli, on ejaculated spermatozoa, and speculate on the possible effects of normal vaginal microbiota on female fertility.


2021 ◽  
Vol 12 ◽  
Author(s):  
Pantelis T. Nikolaidis ◽  
Thomas Rosemann ◽  
Beat Knechtle

AimDespite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”MethodsRecreational male marathon runners (n = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO2max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n = 65).ResultsIn the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO2max (p ≤ 0.003, η2 ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r = −0.36, p < 0.001) and largely with the number of weekly training days (r = 0.52, p < 0.001) and weekly running distance (r = 0.58, p < 0.001), but not with the number of previously finished marathons (r = 0.08, p = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (r = −0.52, p < 0.001), BMI (r = −0.60, p < 0.001), BF (r = −0.65, p < 0.001), VO2max (r = 0.67, p < 0.001), moderately with isometric muscle strength (r = 0.42, p < 0.001), and small with anaerobic muscle power (r = 0.20, p = 0.021). In EXP, race speed could be predicted (R2 = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO2max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, p = 0.122).ConclusionThese findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.


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