Leaf Asymmetry as a Developmental Constraint Imposed by Auxin-Dependent Phyllotactic Patterning

In a majority of species, leaf development is thought to proceed in a bilaterally symmetric fashion without systematic asymmetries. This is despite the left and right sides of an initiating primordium occupying niches that differ in their distance from sinks and sources of auxin. Here, we revisit an existing model of auxin transport sufficient to recreate spiral phyllotactic patterns and find previously overlooked asymmetries between auxin distribution and the centers of leaf primordia. We show that it is the direction of the phyllotactic spiral that determines the side of the leaf these asymmetries fall on. We empirically confirm the presence of an asymmetric auxin response using a DR5 reporter and observe morphological asymmetries in young leaf primordia. Notably, these morphological asymmetries persist in mature leaves, and we observe left-right asymmetries in the superficially bilaterally symmetric leaves of tomato (Solanum lycopersicum) and Arabidopsis thaliana that are consistent with modeled predictions. We further demonstrate that auxin application to a single side of a leaf primordium is sufficient to recapitulate the asymmetries we observe. Our results provide a framework to study a previously overlooked developmental axis and provide insights into the developmental constraints imposed upon leaf morphology by auxin-dependent phyllotactic patterning.

Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

Accelerometry and Energy Expenditure in Obese Adults and Normal Weight Controls

Objective: To evaluate a new triaxial accelerometer device for prediction of energy expenditure, measured as VO2/kg, in obese adults and normal-weight controls during activities of daily life. Subjects and methods: Thirty-seven obese adults (Body Mass Index (BMI) 37±5.4) and seventeen controls (BMI 23±1.8) performed eight activities for 5 to 8 minutes while wearing a triaxial accelerometer on the right thigh. Simultaneously, VO2 and VCO2 were measured using a portable metabolic system. The relationship between accelerometer counts (AC) and VO2/kg was analysed using spline regression and linear mixed-effects models. Results: For all activities, VO2/kg was significantly lower in obese participants than in normalweight controls. A linear relationship between AC and VO2/kg existed only within accelerometer values from 0 to 300 counts/min, with an increase of 3.7 (95%-confidence interval (CI) 3.4 - 4.1) and 3.9 ml/min (95%-CI 3.4 - 4.3) per increase of 100 counts/min in obese and normal-weight adults, respectively. Linear modelling of the whole range yields wide prediction intervals for VO2/kg of ± 6.3 and ±7.3 ml/min in both groups. Conclusion: In obese and normal-weight adults, the use of AC for predicting energy expenditure, defined as VO2/kg, from a broad range of physical activities, characterized by varying intensities and types of muscle work, is limited.

The energy expenditure of 2 Holstein cow strains in an organic grazing system

Until recently, measurements of energy expenditure (EE; herein defined as heat production) in respiration chambers did not account for the extra energy requirements of grazing dairy cows on pasture. As energy is first limiting in most pasture-based milk production systems, its efficient use is important. Therefore, the aim of the present study was to compare EE, which can be affected by differences in body weight (BW), body composition, grazing behavior, physical activity, and milk production level, in 2 Holstein cow strains. Twelve Swiss Holstein-Friesian (HCH; 616 kg of BW) and 12 New Zealand Holstein-Friesian (HNZ; 570 kg of BW) cows in the third stage of lactation were paired according to their stage of lactation and kept in a rotational, full-time grazing system without concentrate supplementation. After adaption, the daily milk yield, grass intake using the alkane double-indicator technique, nutrient digestibility, physical activity, and grazing behavior recorded by an automatic jaw movement recorder were investigated over 7d. Using the (13)C bicarbonate dilution technique in combination with an automatic blood sampling system, EE based on measured carbon dioxide production was determined in 1 cow pair per day between 0800 to 1400 h. The HCH were heavier and had a lower body condition score compared with HNZ, but the difference in BW was smaller compared with former studies. Milk production, grass intake, and nutrient digestibility did not differ between the 2 cow strains, but HCH grazed for a longer time during the 6-h measurement period and performed more grazing mastication compared with the HNZ. No difference was found between the 2 cow strains with regard to EE (291 ± 15.6 kJ) per kilogram of metabolic BW, mainly due to a high between-animal variation in EE. As efficiency and energy use are important in sustainable, pasture-based, organic milk production systems, the determining factors for EE, such as methodology, genetics, physical activity, grazing behavior, and pasture quality, should be investigated and quantified in more detail in future studies.

Comparison of energy expenditure, eating pattern and physical activity of grazing and zero-grazing dairy cows at different time points during lactation

An experiment was conducted to determine the effect of grazing versus zero-grazing on energy expenditure (EE), feeding behaviour and physical activity in dairy cows at different stages of lactation. Fourteen Holstein cows were subjected to two treatments in a repeated crossover design with three experimental series (S1, S2, and S3) reflecting increased days in milk (DIM). At the beginning of each series, cows were on average at 38, 94 and 171 (standard deviation (SD) 10.8) DIM, respectively. Each series consisted of two periods containing a 7-d adaptation and a 7-d collection period each. Cows either grazed on pasture for 16–18.5 h per day or were kept in a freestall barn and had ad libitum access to herbage harvested from the same paddock. Herbage intake was estimated using the double alkane technique. On each day of the collection period, EE of one cow in the barn and of one cow on pasture was determined for 6 h by using the 13C bicarbonate dilution technique, with blood sample collection done either manually in the barn or using an automatic sampling system on pasture. Furthermore, during each collection period physical activity and feeding behaviour of cows were recorded over 3 d using pedometers and behaviour recorders. Milk yield decreased with increasing DIM (P<0.001) but was similar with both treatments. Herbage intake was lower (P<0.01) for grazing cows (16.8 kg dry matter (DM)/d) compared to zero-grazing cows (18.9 kg DM/d). The lowest (P<0.001) intake was observed in S1 and similar intakes were observed in S2 and S3. Within the 6-h measurement period, grazing cows expended 19% more (P<0.001) energy (319 versus 269 kJ/kg metabolic body size (BW0.75)) than zero-grazing cows and differences in EE did not change with increasing DIM. Grazing cows spent proportionally more (P<0.001) time walking and less time standing (P<0.001) and lying (P<0.05) than zero-grazing cows. The proportion of time spent eating was greater (P<0.001) and that of time spent ruminating was lower (P<0.05) for grazing cows compared to zero-grazing cows. In conclusion, lower feed intake along with the unchanged milk production indicates that grazing cows mobilized body reserves to cover additional energy requirements which were at least partly caused by more physical activity. However, changes in cows׳ behaviour between the considered time points during lactation were too small so that differences in EE remained similar between treatments with increasing DIM.

Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.