A s the most common cancer among women worldwide, breast cancer poses a great challenge to public health on a global scale (1). Identification of the presence of lymph node metastasis is pivotal for the pathologic staging, prognosis, and guidance of treatment in patients with breast cancer (2). Although several histopathologic findings, such as vascular and lymphatic invasion, epithelial hyperplasia, and necrosis, are associated with a higher risk for lymph node metastasis, they are available only postoperatively (3). The preoperative prediction of lymph node metastasis can provide valuable information for determining adjuvant therapy and developing surgical plans, thereby facilitating pretreatment decisions.Preoperative imaging assessment is of great value because of its convenient, comprehensive, and noninvasive properties. US plays a crucial role in detecting breast cancer and predicting lymph node metastasis (4). Most patients with early stage breast cancer who have clinically negative lymph nodes have no suspicious signs at either physical examination or imaging. Although radiologists often cannot find any signs of metastasis on US images of clinically negative lymph nodes, axillary lymph node metastasis is detected with sentinel lymph node biopsy in 15%-20% of patients (5). Several studies have found that numerous breast US characteristics are associated with lymph node metastasis. The distance

Dissipative particle dynamics (DPD) is a mesoscale particle method that bridges the gap between microscopic and macroscopic simulations. It can be regarded as a coarse-grained molecular dynamics method suitable for larger time and length scales. It has been successfully applied to different areas of interests, especially in modeling the hydrodynamic behavior of complex fluids in mesoscale. This paper presents an overview on DPD including the methodology, formulation, implementation procedure and some related numerical aspects. The paper also reviews the major applications of the DPD method, especially in modeling (1) micro drop dynamics, (2) multiphase flows in microchannels and fracture networks, (3) movement and suspension of macromolecules in micro channels and (4) movement and deformation of single cells. The paper ends with some concluding remarks summarizing the major features and future possible development of this unique mesoscale modeling technique.

Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

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Summary Plant organs can adopt a wide range of shapes, resulting from highly directional cell growth and divisions. We focus here on leaves and leaf-like organs in Arabidopsis and tomato, characterized by the formation of thin, flat laminae. Combining experimental approaches with 3D mechanical modeling, we provide evidence that leaf shape depends on cortical microtubule mediated cellulose deposition along the main predicted stress orientations, in particular, along the adaxial-abaxial axis in internal cell walls. This behavior can be explained by a mechanical feedback and has the potential to sustain and even amplify a preexisting degree of flatness, which in turn depends on genes involved in the control of organ polarity and leaf margin formation.

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

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