Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required

Protein alterations associated with temozolomide resistance in subclones of human glioblastoma cell lines

Temozolomide (TMZ) is the standard chemotherapeutic agent for human malignant glioma, but intrinsic or acquired chemoresistance represents a major obstacle to successful treatment of this highly lethal group of tumours. Obtaining better understanding of the molecular mechanisms underlying TMZ resistance in malignant glioma is important for the development of better treatment strategies. We have successfully established a passage control line (D54-C10) and resistant variants (D54-P5 and D54-P10) from the parental TMZ-sensitive malignant glioma cell line D54-C0. The resistant sub-cell lines showed alterations in cell morphology, enhanced cell adhesion, increased migration capacities, and cell cycle arrests. Proteomic analysis identified a set of proteins that showed gradual changes in expression according to their 50% inhibitory concentration (IC50). Successful validation was provided by transcript profiling in another malignant glioma cell line U87-MG and its resistant counterparts. Moreover, three of the identified proteins (vimentin, cathepsin D and prolyl 4-hydroxylase, beta polypeptide) were confirmed to be upregulated in high-grade glioma. Our data suggest that acquired TMZ resistance in human malignant glioma is associated with promotion of malignant phenotypes, and our reported molecular candidates may serve not only as markers of chemoresistance but also as potential therapeutic targets in the treatment of TMZ-resistant human malignant glioma, providing a platform for future investigations