Aortic root measurement on CT: linear dimensions, aortic root area and comparison with echocardiography. A retrospective cross sectional study

Objective: We sought to assess the different CT aortic root measurements and determine their relationship to transthoracic echocardiography (TTE). Methods: TTE and ECG-gated CT images were reviewed from 70 consecutive patients (mean age 54 ± 18 years; 67% male) with tricuspid aortic roots (trileaflet aortic valves) between Nov 2009 and Dec 2013. Three CT planes (coronal, short axis en face and three-chamber) were used for measurement of nine linear dimensions. TTE aortic root dimension was measured as per guidelines from the parasternal long axis view. Results: All CT short axis measurements of the aortic root had excellent reproducibility (intraclass correlation coefficient, ICC 0.96–0.99), while coronal and three-chamber planes had lower reproducibility with ICC 0.90 (95% CI 0.84–0.94) and ICC 0.92 (0.87–0.95) respectively. CT coronal and short axis maximal dimensions were systematically larger than TTE (mean 2 mm larger, p < 0.001), while CT cusp to commissure measurements were systematically smaller (CT RCC-comm mean 2 mm smaller than TTE, p < 0.001). All CT short axis measurements had excellent correlation with aortic root area with CT short axis maximal dimension marginally better than the rest (Pearson’s R 0.97). Conclusion: Systematic differences exist between CT and TTE dependent on the CT plane of measurement. All CT short axis measurements of the aortic root had excellent reproducibility and correlation with aortic root area with maximal dimension appearing marginally better than the rest. Our findings highlight the importance of specifying the chosen plane of aortic root measurement on CT. Advances in knowledge: Systematic differences in aortic root dimension exist between TTE and the various CT measurement planes. CT coronal and short axis maximal dimensions were systematically larger than TTE, while CT cusp to commissure measurements were smaller. CT readers should indicate the plane of measurement and the specific linear dimension to avoid ambiguity in follow-up and comparison.

Digging roots is easier with AI

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R 2=0.99), profile wall (R 2=0.76) and core-break (R 2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD: cm cm -3) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1 to 5 cm cm -3) as well as at low RLD (0.1 to 0.3 cm cm -3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Digging roots is easier with AI

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Suitability Evaluation of a Train’s Scheduled Section Travel Time

Two methods used to evaluate the suitability of a train’s scheduled section travel time (TSSTT) are theoretical modeling and data analysis. The first is suitable for newly constructed railway projects, the second can reveal the reliability of the train section running time (TSRT) under an instruction of TSSTT in cases where the train operation data are provided. A suitability evaluation method of TSSTT is proposed by calculating the possibility that a train completes a task within the time windows, centering on the TSSTT given in advance. The TSRTs between two adjacent stations are classified into four groups based on whether the train dwells at the two end stations of the railway section, and then subdivided secondly into subgroups by the instruction of TSSTT given. The kurtosis of each subgroup data of TSRT is larger than 3, so Weibull distribution is selected to fit the TSRT distribution of subgroup data due to good fitness based on root measurement of the least square (SRLSM). A busy high-speed railway line in the Wuhan area of China is used to validate the presented approach. Each railway section has its own suitable TSSTT in which TSRT might achieve 96% reliability of arriving within 2.5 minutes centering on suitable TSSTT, otherwise which might not obtain 10% reliability.

Weighted quantum measurements

In the paper, the Belavkin weighted square root measurement in infinite dimension is investigated. The question of uniqueness of such measurement is analyzed and some estimates for the probability of detection are obtained. Moreover, the asymptotics of the probability of detection and the probability of failure are derived in the situation when the pure states approach an orthonormal basis. The results in the paper generalize those obtained earlier for finite dimension.

Union bound for quantum information processing

In this paper, we prove a quantum union bound that is relevant when performing a sequence of binary-outcome quantum measurements on a quantum state. The quantum union bound proved here involves a tunable parameter that can be optimized, and this tunable parameter plays a similar role to a parameter involved in the Hayashi–Nagaoka inequality (Hayashi & Nagaoka 2003 IEEE Trans. Inf. Theory 49 , 1753–1768. ( doi:10.1109/TIT.2003.813556 )), used often in quantum information theory when analysing the error probability of a square-root measurement. An advantage of the proof delivered here is that it is elementary, relying only on basic properties of projectors, Pythagoras' theorem, and the Cauchy–Schwarz inequality. As a non-trivial application of our quantum union bound, we prove that a sequential decoding strategy for classical communication over a quantum channel achieves a lower bound on the channel's second-order coding rate. This demonstrates the advantage of our quantum union bound in the non-asymptotic regime, in which a communication channel is called a finite number of times. We expect that the bound will find a range of applications in quantum communication theory, quantum algorithms and quantum complexity theory.

Rapid hydroponic screening for molybdenum tolerance in rice through morphological and biochemical analysis

High yielding varieties of rice (Oryza sativa) cultivars were tested for their tolerance to different levels of molybdenum (Mo) (0.1&micro;M &ndash; control, 0.2, 0.4, 0.8 and 1.6&micro;M) in nutrient solution at pH 6.8. Seeds of rice were germinated and grown in presence of molybdenum under controlled environmental conditions. Standard growth parameters such as root length, shoot length, root/shoot dry biomass and root/shoot tolerance index were tested as markers of molybdenum toxicity. Measurements as early as 48 hours after the germination did not yield consistent results. However, root measurement on 3^rd, 6^th&nbsp;and 9^th&nbsp;day after root emergence showed significant differences among cultivars of rice. Rice cultivars Annapurna, Kusuma, Deepa and Vaghari developed better root system while, Paridhan-1, Pusa-2-21 and Ratna showed poor growth of the roots in presence (0.8&micro;M) of molybdenum. The root tolerance index (RTI) and the shoot tolerance index (STI) in Annapurna, Kusuma and Deepa in rice were high indicating their tolerance to molybdenum; Paridhan-1 and Ratna, however, showed low RTI and STI. Based on the growth parameters, twenty cultivars of rice were ranked in respect of their tolerance to molybdenum: Annapurrna &gt; Deepa &gt; Kusuma &gt; Vaghari &gt; Hamsa &gt; Vikram &gt; Bharati &gt; Paridhan-2 &gt; Aswathi &gt; Subhadra &gt; Sankar &gt; Sakti &gt; Nilgiri &gt; Rudra &gt; Hema &gt; Pragati &gt; Pusa-2-21 &gt; Ratna &gt; Paridhan-1, respectively. Molybdenum toxicity was correlated with increased peroxidase and catalase activity in different cultivars of rice. This method can be employed for quick screening of rice cultivars for molybdenum tolerance in breeding programmes.