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Tag: University of Zurich

  • AI boosts plant observation precision

    AI boosts plant observation precision

    Newswise — Artificial intelligence (AI) can help plant scientists collect and analyze unprecedented volumes of data, which would not be possible using conventional methods. Researchers at the University of Zurich (UZH) have now used big data, machine learning and field observations in the university’s experimental garden to show how plants respond to changes in the environment.

    Climate change is making it increasingly important to know how plants can survive and thrive in a changing environment. Conventional experiments in the lab have shown that plants accumulate pigments in response to environmental factors. To date, such measurements were made by taking samples, which required a part of the plant to be removed and thus damaged. “This labor-intensive method isn’t viable when thousands or millions of samples are needed. Moreover, taking repeated samples damages the plants, which in turn affects observations of how plants respond to environmental factors. There hasn’t been a suitable method for the long-term observation of individual plants within an ecosystem,” says Reiko Akiyama, first author of the study.

    With the support of UZH’s University Research Priority Program (URPP) “Evolution in Action”, a team of researchers has now developed a method that enables scientists to observe plants in nature with great precision. PlantServation is a method that incorporates robust image-acquisition hardware and deep learning-based software to analyze field images, and it works in any kind of weather.

    Millions of images support evolutionary hypothesis of robustness

    Using PlantServation, the researchers collected (top-view) images of Arabidopsis plants on the experimental plots of UZH’s Irchel Campus across three field seasons (lasting five months from fall to spring) and then analyzed the more than four million images using machine learning. The data recorded the species-specific accumulation of a plant pigment called “anthocyanin” as a response to seasonal and annual fluctuations in temperature, light intensity and precipitation.

    PlantServation also enabled the scientists to experimentally replicate what happens after the natural speciation of a hybrid polyploid species. These species develop from a duplication of the entire genome of their ancestors, a common type of species diversification in plants. Many wild and cultivated plants such as wheat and coffee originated in this way.

    In the current study, the anthocyanin content of the hybrid polyploid species A. kamchatica resembled that of its two ancestors: from fall to winter its anthocyanin content was similar to that of the ancestor species originating from a warm region, and from winter to spring it resembled the other species from a colder region. “The results of the study thus confirm that these hybrid polyploids combine the environmental responses of their progenitors, which supports a long-standing hypothesis about the evolution of polyploids,” says Rie Shimizu-Inatsugi, one of the study’s two corresponding authors.

    From Irchel Campus to far-flung regions

    PlantServation was developed in the experimental garden at UZH’s Irchel Campus. “It was crucial for us to be able to use the garden on Irchel Campus to develop PlantServation’s hardware and software, but its application goes even further: when combined with solar power, its hardware can be used even in remote sites. With its economical and robust hardware and open-source software, PlantServation paves the way for many more future biodiversity studies that use AI to investigate plants other than Arabidopsis – from crops such as wheat to wild plants that play a key role for the environment,” says Kentaro Shimizu, corresponding author and co-director of the URPP Evolution in Action.

    The project is an interdisciplinary collaboration with LPIXEL, a company that specializes in AI image analysis, and Japanese research institutes at Kyoto University and the University of Tokyo, among others, under the Global Strategy and Partnerships Funding Scheme of UZH Global Affairs and the International Leading Research grant program of the Japan Society for the Promotion of Science (JSPS). The project also received funding from the Swiss National Science Foundation (SNSF).

    Strategic Partnership with Kyoto University

    Kyoto University is one of UZH’s strategic partner universities. The strategic partnership ensures that high-potential research collaborations will receive the necessary support to thrive, for instance through the UZH Global Strategy and Partnership Funding Scheme. Over the last years, several joint research projects between Kyoto University and UZH have already received funding, among them “PlantServation”.

    University of Zurich

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  • AI offers hope to patients with lyosomal storage disease

    AI offers hope to patients with lyosomal storage disease

    Newswise — Artificial intelligence is becoming increasingly important in drug discovery. Advances in the use of Big Data, learning algorithms and powerful computers have now enabled researchers at the University of Zurich (UZH) to better understand a serious metabolic disease. 

    Cystinosis is a rare lyosomal storage disorder affecting around 1 in 100,000 to 200,000 newborns worldwide. Nephropathic (non-inflammatory) cystinosis, the most common and severe form of the disease, manifests with kidney disease symptoms during the first months of life, often leading to kidney failure before the age of 10. “Children with cystinosis suffer from a devastating, multisystemic disease, and there are currently no available curative treatments,” says Olivier Devuyst, head of the Mechanisms of Inherited Kidney Disorders (MIKADO) group and co-director of the ITINERARE University Research Priority Program at UZH.

    The UZH researchers worked with Insilico Medicine, a company that uses AI for drug discovery, to uncover the underlying cellular mechanism behind kidney disease in cystinosis. Leveraging model systems and Insilico’s PandaOmics platform, they identified the disease-causing pathways and prioritized therapeutic targets within cystinosis cells. Their findings revealed a causal association between the regulation of a protein called mTORC1 and the disease. Alessandro Luciani, one of the research group leaders, explains: “Our research showed that cystine storage stimulates the activation of the mTORC1 protein, leading to the impairment of kidney tubular cell differentiation and function.”

    Promising drug identified for treatment

    As patients with cystinosis often require a kidney transplant to restore kidney function, there is an urgent need for more effective treatments. Utilizing the PandaOmics platform, the UZH research team therefore embarked on a search for existing drugs that could be repurposed for cystinosis. This involved an analysis of the drugs’ structure, target enzymes, potential side effects and efficacy in the affected tissues. The already-licensed drug rapamycin was identified as a promising candidate for treating cystinosis. Studies in cell systems and model organisms confirmed that treatment with rapamycin restored the activity of lysosomes and rescued the cellular functions.

    Olivier Devuyst and Alessandro Luciani are optimistic about future developments: “Although the therapeutic benefits of this approach will require further clinical investigations, we believe that these results, obtained through unique interdisciplinary collaboration, bring us closer to a feasible therapy for cystinosis patients.”

    Study participants

    Scientists from the University of Zurich (UZH), the Faculty of Medicine at UCLouvain in Brussels, the Microsoft Research-University of Trento Centre for Computational and Systems Biology, and the company Insilico Medicine were involved in the study. The USA’s Cystinosis Research Foundation and the Swiss National Science Foundation (SNSF) provided funding for the study.

    University of Zurich

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