Rootstock and Crop Load Effects on 'Honeycrisp' Photosynthetic Performance and Carbohydrate Accumulation.

Rootstock selection and crop load adjustment are key practices in apple orchard management; nevertheless, the effects of rootstocks and crop load levels on important physiological processes of the scions, such as photosynthetic performance and carbohydrate accumulation, are still unclear. To investigate the impact of different rootstocks and crop load levels on scion photosynthesis and carbohydrate buildup, in 2020, 'Honeycrisp' trees grafted on rootstocks 'G.41', 'G.935', and 'M.9-T337' were thinned to low and high crop load levels, and photosynthetic performance and carbohydrate accumulation in leaves and fruit were evaluated. Leaves from 'G.935' showed the highest net photosynthesis and electron use efficiency of photosynthesis and the lowest activity for non-net carboxylative processes, all together indicative of enhanced photosynthetic performance. High crop load determined an increase in gas exchange, suggesting a positive feedback of high fruit competition on carbon assimilation. While rootstock 'M.9-T337' showed a higher accumulation of starch in leaves, no pattern regarding the composition of leaf-soluble sugars among rootstocks could be identified. Conversely, by the end of the harvest season, leaves from low-cropping trees had higher fructose, glucose, and sorbitol than those from high-cropping trees, but differences in starch content were not significant. Fructose and sorbitol concentrations were affected by rootstock and crop load, respectively. Overall, this study showed that high cropping enhanced photosynthesis in 'Honeycrisp' apple and determined lower accumulation of some soluble carbohydrates (fructose, glucose, sorbitol) in leaves. This study also provided insights into how rootstocks affect photosynthetic performance of 'Honeycrisp', highlighting 'G.935' as the rootstock conferring the highest photosynthetic capacity under the present experimental conditions.

Effective Pollination Period and Parentage Effect on Pollen Tube Growth in Apple.

Flower receptivity is a limiting factor for the fertilization of several tree fruit. The effective pollination period (EPP) can be used to determine flower longevity and identify limiting factors by assessing stigmatic receptivity, pollen tube growth rate, and ovule longevity. EPPs were determined for three apple cultivars under natural field conditions in Washington State in 2019 and 2020. In addition, a greenhouse study, performed under semi-controlled conditions, evaluated the influence of six maternal parents on the pollen tube growth performance of six pollen sources. The duration of the stigmatic receptivity ranged from 6.3 to 8.1 days, depending on the cultivar and year-pollen tubes required between 5.5 and 7.0 days from the stigma to reach the ovules. Ovule longevity of non-pollinated flowers varied between 8.2 and 11.3 days. Combinations of these factors resulted in EPPs ranging from 3.0 days for 'Rubinstar' to 5.6 days for 'Olsentwo Gala' in the present experimental conditions. The greenhouse study revealed that parentage affected pollen tube growth performance. Importantly, a significant interaction between maternal and paternal factors indicated that the performance of different pollen sources depended on the maternal parent and that general recommendations on pollination need to account for the maternal parent.

Novel Metrics to Characterize In Vitro Pollen Tube Growth Performance of Apple Cultivars.

In vitro germination assays are frequently used in screening trials to evaluate the pollen viability of pollinizers. To be effective, screening trials must have defined threshold criteria, from which individuals can then be assessed. However, despite decades of research on pollen viability, no established threshold is available to categorize apple cultivars based on their in vitro pollen tube lengths. This study aimed to identify and characterize the subgroups of cultivars based on their pollen tube growth performance. In vitro pollen tube lengths of 41 individuals were determined by incubating samples on artificial germination media at 15 and 25 °C. A six-number summary statistic was calculated, and hierarchical clustering on principal component (HCPC) analysis was used to determine and characterize subgroups. Furthermore, a decision tree model was used to predict class membership for future datasets. HCPC analysis partitioned the 41 individuals into three subgroups with different performances. The decision tree quickly predicted the cluster membership based on the second quartile at 15 °C and the third quartile at 25 °C. The thresholds from the decision tree can be used to characterize new observations. The use of the methods will be demonstrated using a case study with 29 apple accessions.

Assessment of maternal drug intake by urinary bio monitoring during pregnancy and postpartally until the third perinatal year.

PURPOSE: Although sales of prescribed and over-the-counter (OTC) medication are rising, little is known about individual drug intake. This study was aimed to obtain complementary information about drug intake. METHOD: Information on drug utilization was obtained in a female cohort for five different time points (TP): 36th week of pregnancy (n = 622), 7th perinatal week (n = 533), 3rd perinatal month (n = 340), and 1st perinatal (n = 534) and 3rd perinatal year (n = 324) by a validated urine screening method. RESULTS: Drugs were detected 807 times among all analyzed samples (n = 2353) with less drug intake for early TP compared with later TP (~24.4%, n = 152; ~33.8%, n = 180; ~23.2%, n = 79; ~42.5%, n = 227; and ~52.2%, n = 169). The diversity of drugs increased from 25 up to 40 different drugs for the investigated period. OTC drugs were detected most frequently reflected by the top three drugs: acetaminophen (~37%, n = 292), ibuprofen (~23%, n = 183), and xylometazoline (~12%, n = 98). Mainly guideline-orientated drug therapy was observed. However, contraindicated ibuprofen intake during third trimester urine samples (n = 26) and a repeated usage of acetaminophen and/or ibuprofen (n = 9), as well as xylometazoline (n = 7), reveal missing information about drug safety. CONCLUSION: Bio monitoring was applied for detection of drug intake revealing a lack of information about OTC products and their health risks. Hence, information about health risks for certain drugs and patient groups must be improved for and by pharmacists, to avoid (i) usage of contraindicated drugs and (ii) abuse of OTC drugs.

Validation of a multi-analyte HPLC-DAD method for determination of uric acid, creatinine, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid and 2-methylhippuric acid in human urine.

During the last decades exposure sciences and epidemiological studies attracts more attention to unravel the mechanisms for the development of chronic diseases. According to this an existing HPLC-DAD method for determination of creatinine in urine samples was expended for seven analytes and validated. Creatinine, uric acid, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid, and 2-methylhippuric acid were separated by gradient elution (formate buffer/methanol) using an Eclipse Plus C18 Rapid Resolution column (4.6mm×100mm). No interfering signals were detected in mobile phase. After injection of blank urine samples signals for the endogenous compounds but no interferences were detected. All analytes were linear in the selected calibration range and a non weighted calibration model was chosen. Bias, intra-day and inter-day precision for all analytes were below 20% for quality control (QC) low and below 10% for QC medium and high. The limits of quantification in mobile phase were in line with reported reference values but had to be adjusted in urine for homovanillic acid (45mg/L), niacinamide 58.5(mg/L), and indole-3-acetic acid (63mg/L). Comparison of creatinine data obtained by the existing method with those of the developed method showing differences from -120mg/L to +110mg/L with a mean of differences of 29.0mg/L for 50 authentic urine samples. Analyzing 50 authentic urine samples, uric acid, creatinine, hippuric acid, and 2-methylhippuric acid were detected in (nearly) all samples. However, homovanillic acid was detected in 40%, niacinamide in 4% and indole-3-acetic acid was never detected within the selected samples.

Monitoring of drug intake during pregnancy by questionnaires and LC-MS/MS drug urine screening: evaluation of both monitoring methods.

Various studies pointed towards a relationship between chronic diseases such as asthma and allergy and environmental risk factors, which are one aspect of the so-called Exposome. These environmental risk factors include also the intake of drugs. One critical step in human development is the prenatal period, in which exposures might have critical impact on the child's health outcome. Thereby, the health effects of drugs taken during gestation are discussed controversially with regard to newborns' disease risk. Due to this, the drug intake of pregnant women in the third trimester was monitored by questionnaire, in addition to biomonitoring using a local birth cohort study, allowing correlations of drug exposure with disease risk. Therefore, 622 urine samples were analyzed by an untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) urine screening and the results were compared to self-administered questionnaires. In total, 48% (n = 296) reported an intake of pharmaceuticals, with analgesics as the most frequent reported drug class in addition to dietary supplements. 182 times compounds were detected by urine screening, with analgesics (42%; n = 66) as the predominantly drug class. A comparison of reported and detected drug intake was performed for three different time spans between completion of the questionnaires and urine sampling. Even if the level of accordance was low in general, similar percentages (~25%, ~19%, and ~ 20%) were found for all groups. This study illustrates that a comprehensive evaluation of drug intake is neither achieved by questionnaires nor by biomonitoring alone. Instead, a combination of both monitoring methods, providing complementary information, should be considered.