👤 Kamran Irfan

Computer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 ​%. However, the precision for stems with 54 ​% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies. Show less

Oxidative stress (OS) and c-Jun N-terminal kinase (JNK) are both key indicators implicated in neuro-inflammatory signalling pathways and their respective neurodegenerative diseases. Drugs targeting these factors can be considered as suitable candidates for treatment of neuronal dysfunction and memory impairment. The present study encompasses beneficial effects of a naturally occurring triterpenoid, friedelin, against scopolamine-induced oxidative stress and neurodegenerative pathologies in mice models. The treated animals were subjected to behavioural tests i.e., Y-maze and Morris water maze (MWM) for memory dysfunction. The underlying mechanism was determined via western blotting, antioxidant enzymes and lipid profile analyses. Molecular docking studies were carried out to predict the binding modes of friedelin in the binding pocket of p-JNK protein. The results reveal that scopolamine caused oxidative stress by (1) inhibiting catalase (CAT), peroxidase enzyme (POD), superoxide dismutase (SOD), and reduced glutathione enzyme (GSH); (2) the up-regulation of thiobarbituric acid reactive substances (TBARS) in mice brain; and (3) affecting the neuronal synapse (both pre- and post-synapse) followed by associated memory dysfunction. In contrast, friedelin administration not only abolished scopolamine-induced oxidative stress, glial cell activation, and neuro-inflammation but also inhibited p-JNK and NF-κB and their downstream signaling molecules. Moreover, friedelin administration improved neuronal synapse and reversed scopolamine-induced memory impairment accompanied by the inhibition of β-secretase enzyme (BACE-1) to halt amyloidogenic pathways of amyloid-β production. In summary, all of the results show that friedelin is a potent naturally isolated neuro-therapeutic agent to reverse scopolamine-induced neuropathology, which is characteristic of Alzheimer's disease. Show less