Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching.

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.

Caricature Expression Extrapolation Based on Kendall Shape Space Theory.

Facial expression editing plays a fundamental role in facial expression generation and has been widely applied in modern film productions and computer games. While the existing 2-D caricature facial expression editing methods are mostly realized by expression interpolation from the original image to the target image, expression extrapolation has rarely been studied before. In this article, we propose a novel expression extrapolation method for caricature facial expressions based on the Kendall shape space, in which the key idea is to introduce a representation for the 3-D expression model to remove rigid transformations, such as translation, scaling, and rotation, from the Kendall shape space. Built upon the proposed representation, the 2-D caricature expression extrapolation process can be controlled by the 3-D model reconstructed from the input 2-D caricature image and the exaggerated expressions of the caricature images generated based on the extrapolated expression of a 3-D model that is robust to facial poses in the Kendall shape space; this 3-D model can be calculated with tools such as exponential mapping in Riemannian space. The experimental results demonstrate that our method can effectively and automatically extrapolate facial expressions in caricatures with high consistency and fidelity. In addition, we derive 3-D facial models with diverse expressions and expand the scale of the original FaceWarehouse database. Furthermore, compared with the deep learning method, our approach is based on standard face datasets and avoids the construction of complicated 3-D caricature training sets.

Granular, Slow-Release Fertilizer from Urea-formaldehyde, Ammonium Polyphosphate, and Amorphous Silica Gel: A New Strategy Using Cold Extrusion.

A new granular, slow-release fertilizer prepared by a cold-extrusion strategy (GSRFEx) based on urea-formaldehyde (UF), ammonium polyphosphate (APP), and amorphous silica gel (ASG) was presented. Characterizations showed that there were strong hydrogen-bond interactions and good compatibility among UF, APP, and ASG in GSRFEx. The mechanical properties as well as the slow-release properties of GSRFEx were greatly enhanced after the addition of APP and ASG to UF. Rape pot experiments indicated that GSRFEx could improve N-use efficiency dramatically and thereby facilitate the growth of rape. Importantly, as an economical, effective, and environment-friendly technology, cold extrusion has great potential to be applied in horticulture and agriculture. We hope that our work can offer an alternative method for the design of slow-release fertilizers with desirable properties.

Preparation and Properties of a Novel Semi-IPN Slow-Release Fertilizer with the Function of Water Retention.

A new semi-interpenetrating polymer network (semi-IPN) slow-release fertilizer (SISRF) with water absorbency, based on the kaolin-g-poly(acrylic acid-co-acrylic amide) (kaolin-g-P(AA-co-AM)) network and linear urea-formaldehyde oligomers (UF), was prepared by solution polymerization. Nutrients phosphorus and potassium were supplied by adding dipotassium hydrogen phosphate during the preparation process. The structure and properties of SISRF were characterized by various characterization methods. SISRF showed excellent water absorbency of 68 g g-1 in tap water. The slow-release behavior of nutrients and water-retention capacity of SISRF were also measured. Meanwhile, the swelling kinetics was well described by a pseudo-second-order kinetics model. Results suggested the formation of SISRF with simultaneously good slow-release and water-retention capacity, which was expected to apply in modern agriculture and horticulture.