An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.

Wheat exhibits complex characteristics during its growth, such as extensive tillering, slender and soft leaves, and severe organ cross-obscuration, posing a considerable challenge in full-cycle phenotypic monitoring. To address this, this study presents a synthesized method based on SFM-MVS (Structure-from-Motion, Multi-View Stereo) processing for handling and segmenting wheat point clouds, covering the entire growth cycle from seedling to grain filling stages. First, a multi-view image acquisition platform was constructed to capture image sequences of wheat plants, and dense point clouds were generated using SFM-MVS technology.

Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma.

Developing a radiomic model to predict CTLA4 expression levels and assessing its prognostic accuracy for patients. Medical imaging data were sourced from the TCIA database, while transcriptome sequencing data were derived from the TCGA database. We utilized a linear kernel SVM algorithm to develop a radiomic model for predicting CTLA4 gene expression.

Rodent hole detection in a typical steppe ecosystem using UAS and deep learning.

Introduction: Rodent outbreak is the main biological disaster in grassland ecosystems. Traditional rodent damage monitoring approaches mainly depend on costly field surveys, e.g.

Differential proteomic screening and identification for non-traumatic necrotic femoral osseous tissue.

Currently, there is a lack of effective early screening and detection methods for femoral head necrosis. Current research on most orthopedic diseases focuses on proteomics in the preliminary stage. The recent fluorescence differential in gel electrophoresis (DIGE) has advantages such as a high reproducibility, high sensitivity, high throughput, and high dynamic range.

A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems.

With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented.