Crop phenotyping is a major bottleneck in current plant research. Field-based high-throughput phenotyping platforms are an important prerequisite to advance crop breeding. We developed a cable-suspended field phenotyping platform covering an area of ~1ha. The system operates from 2 to 5m above the canopy, enabling a high image resolution. It can carry payloads of up to 12kg and can be operated under adverse weather conditions. This ensures regular measurements throughout the growing period even during cold, windy and moist conditions. Multiple sensors capture the reflectance spectrum, temperature, height or architecture of the canopy. Monitoring from early development to maturity at high temporal resolution allows the determination of dynamic traits and their correlation to environmental conditions throughout the entire season. We demonstrate the capabilities of the system with respect to monitoring canopy cover, canopy height and traits related to thermal and multi-spectral imaging by selected examples from winter wheat, maize and soybean. The system is discussed in the context of other, recently established field phenotyping approaches; such as ground-operating or aerial vehicles, which impose traffic on the field or require a higher distance to the canopy.
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Background: OLX-07010 is an oral small molecule inhibitor of tau self-association that prevented the accumulation of tau aggregates in the htau mouse model expressing wild type human CNS tau isoforms and in P301L tau JNPL3 mice using chronic treatment by administration in diet (Davidowitz et al., 2020, PMID: 31771053; 2023 PMID:37556474). A therapeutic study of JNPL3 mice with chronic treatment from 7-12 months of age inhibited the progression of tau aggregation and improved motor coordination.
View Article and Find Full Text PDFAndrology
January 2025
Chair of Human Genetics, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.
Across six decades, androgenetics has consistently concentrated on discovering genetic causes and enhancing the molecular diagnostics of male infertility, disorders of sex development, and their broader implications on health, such as cancer and other comorbidities. Despite vast clinical knowledge, the training of andrologists often lacks fundamental basics in medical genetics. This work, as part of the Special Issue of Andrology "Genetics in Andrology", provides the core terminology in medical genetics and technological advancements in genomics, required to understand the ever-progressing research in the field.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Neurosurgery, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Background: Malignant brain tumors are among the most lethal cancers. Recent studies emphasized the crucial involvement of the immune system, especially T cells, in driving tumor progression and influencing patient outcomes. The emerging field of immunometabolism has shown that metabolic pathways play a pivotal role in regulating immune responses within the tumor microenvironment.
View Article and Find Full Text PDFSci Rep
January 2025
XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China.
High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling.
View Article and Find Full Text PDFPlant Genome
March 2025
INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Université Paris-Saclay, Gif-sur-Yvette, France.
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization.
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