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Many cross-sectional shapes of plants have been found to approximate a superellipse rather than an ellipse. Square bamboos, belonging to the genus Chimonobambusa (Poaceae), are a group of plants with round-edged square-like culm cross sections. The initial application of superellipses to model these culm cross sections has focused on Chimonobambusa quadrangularis (Franceschi) Makino. However, there is a need for large scale empirical data to confirm this hypothesis. In this study, approximately 750 cross sections from 30 culms of C. utilis were scanned to obtain cross-sectional boundary coordinates. A superellipse exhibits a centrosymmetry, but in nature the cross sections of culms usually deviate from a standard circle, ellipse, or superellipse because of the influences of the environment and terrain, resulting in different bending and torsion forces during growth. Thus, more natural cross-sectional shapes appear to have the form of a deformed superellipse. The superellipse equation with a deformation parameter (SEDP) was used to fit boundary data. We find that the cross-sectional shapes (including outer and inner rings) of C. utilis can be well described by SEDP. The adjusted root-mean-square error of SEDP is smaller than that of the superellipse equation without a deformation parameter. A major finding is that the cross-sectional shapes can be divided into two types of superellipse curves: hyperellipses and hypoellipses, even for cross sections from the same culm. There are two proportional relationships between ring area and the product of ring length and width for both the outer and inner rings. The proportionality coefficients are significantly different, as a consequence of the two different superellipse types (i.e., hyperellipses and hypoellipses). The difference in the proportionality coefficients between hyperellipses and hypoellipses for outer rings is greater than that for inner rings. This work informs our understanding and quantifying of the longitudinal deformation of plant stems for future studies to assess the influences of the environment on stem development. This work is also informative for understanding the deviation of natural shapes from a strict rotational symmetry.

Leaves, as the most important photosynthetic organ of plants, are intimately associated with plant function and adaptation to environmental changes. The scaling relationship of the leaf dry mass (or the fresh mass) vs. leaf surface area has been referred to as “diminishing returns”, suggesting that the leaf area fails to increase in proportion to leaf dry mass (or fresh mass). However, previous studies used materials across different families, and there is lack of studies testing whether leaf fresh mass is proportional to the leaf dry mass for the species in the same family, and examining the influence of the scaling of leaf dry mass vs. fresh mass on two kinds of diminishing returns based on leaf dry mass and fresh mass. Bamboo plants (Poaceae: Bambusoideae) are good materials for doing such a study, which have astonishingly similar leaf shapes across species. Bamboo leaves have a typical parallel venation pattern. In general, a parallel venation pattern tends to produce a more stable symmetrical leaf shape than the pinnate and palmate venation patterns. The symmetrical parallel veins enable leaves to more regularly hold water, which is more likely to result in a proportional relationship between the leaf dry mass and absolute water content, which consequently determines whether the scaling exponent of the leaf dry mass vs. area is significantly different from (or the same as) that of the leaf fresh mass vs. area. In the present study, we used the data of 101 bamboo species, cultivars, forms and varieties (referred to as 101 (bamboo) taxa below for convenience) to analyze the scaling relationships between the leaf dry mass and area, and between leaf fresh mass and area. We found that the confidence intervals of the scaling exponents of the leaf fresh mass vs. dry mass of 68 out of the 101 taxa included unity, which indicates that for most bamboo species (67.3%), the increase in leaf water mass keeps pace with that of leaf dry mass. There was a significant scaling relationship between either leaf dry mass or fresh mass, and the leaf surface area for each studied species. We found that there was no significant difference between the scaling exponent of the leaf dry mass vs. leaf area and that of the leaf fresh mass vs. leaf area when the leaf dry mass was proportional to the leaf fresh mass. The goodness of fit to the linearized scaling relationship of the leaf fresh mass vs. area was better than that of the leaf dry mass vs. area for each of the 101 bamboo taxa. In addition, there were significant differences in the normalized constants of the leaf dry mass vs. fresh mass among the taxa (i.e., the differences in leaf water content), which implies the difference in the adaptabilities to different environments across the taxa.

25Assigning cell identities in dense image stacks is critical for many applications, for comparing 26 data across animals and experiment conditions, and investigating properties of specific cells. 27Conventional methods are laborious, require experience, and could introduce bias. We present 28 a generalizable framework based on Conditional Random Fields models for automatic cell 29 identification. This approach searches for optimal arrangements of labels that maximally 30 preserves prior knowledge such as geometrical relationships. The algorithm shows better 31 accuracy and more robust handling of perturbations, e.g. missing cells and position variability, 32 with both synthetic and experimental ground-truth data. The framework is generalizable across 33 strains, imaging conditions, and easily builds and utilizes active data-driven atlases, which 34 further improves accuracy. We demonstrate the utility in gene-expression pattern analysis, 35 multi-cellular calcium imaging, and whole-brain imaging experiments. Thus, our framework is 36 highly valuable to a wide variety of annotation scenarios including in zebrafish, Drosophila, 37 hydra, and mouse brains. 38 39 6 define cell specific features (unary potentials) and co-dependent features (pairwise potentials) 104 in the model. The basic model uses several pairwise relationship features for all pairs of cells, 105including binary positional relationships, angular relationship, and the Gromov-Wasserstein 106 discrepancy between cells in the image and an atlas. By encoding these features among all pairs 107 of cells, our fully-connected CRF model accounts for label dependencies between each cell pair 108 to maximize accuracy. Third, identities are automatically predicted for all neurons iteratively, 109 taking into account neurons missing in the image stack ( Supplementary Note 1.4). Duplicate 110 assignments are handled by calculating a label-consistency score for each neuron, removing 111

Background It is a clinical problem to identify histological component in enlarged cervical lymph nodes, particularly in differentiation between lymph node metastasis and lymphoma involvement. Purpose To construct two kinds of deep learning (DL)‐based computer‐aided diagnosis (CAD) systems including DL‐convolutional neural networks (DL‐CNN) and DL‐machine learning for pathological diagnosis of cervical lymph nodes by positron emission tomography (PET)/computed tomography (CT) images. Methods We collected CT, PET, and PET/CT images series from 165 patients with enlarged cervical lymph nodes receiving examinations from January 2014 to June 2018. Six CNNs pretrained on ImageNet as DL architectures were used for two kinds of DL‐based CAD models, including DL‐CNN and DL‐machine learning models. The DL‐CNN models were constructed via transfer learning for classification of lymphomatous and metastatic lymph nodes. The DL‐machine learning models were developed by DL‐based features extractors and support vector machine (SVM) classifier. As for DL‐SVM models, we also evaluate the effect of handcrafted radiomics features in combination of DL‐based features. Results The DL‐CNN model with ResNet50 architecture on PET/CT images had the best diagnostic performance among all six algorithms with an area under the receiver operating characteristic curve (AUC) of 0.845 and accuracy of 78.13% in the testing cohort. The DL‐SVM model on ResNet50 extractor showed great performance for the testing cohort with an AUC of 0.901, accuracy of 86.96%, sensitivity of 76.09%, and specificity of 94.20%. The combination of DL‐based and handcrafted features yielded the improvement of diagnostic performance. Conclusions Our DL‐based CAD systems on PET/CT images were developed for classifying metastatic and lymphomatous involvement with favorable diagnostic performance in enlarged cervical lymph nodes. Further clinical practice of our systems may improve quality of the following therapeutic interventions and optimize patients’ outcomes.

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