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Knowledge about the drivers of vegetation dynamics in grasslands is fundamental to select appropriate management for conservation purposes. In this study, we provide a detailed analysis of vegetation dynamics in alkali grasslands, a priority habitat of the Natura 2000 network. We studied vegetation dynamics in five stands of four alkali grassland types in the Hortobágy National Park (eastern Hungary), between 2009 and 2011. We analysed the effect of fluctuations in precipitation on both the overall vegetation composition and on the cover of each species using Self Organizing Map neural networks (SOM). We found that SOM is a promising tool to reveal plant community dynamics. As we analysed species cover and overall vegetation composition separately, we were able to identify the species re-sponsible for particular vegetation changes. Fluctuations in precipitation (a dry season, followed by a wet and an average season) caused quick shifts in plant species composition because of an increasing cover of halophyte forbs, probably because of salinisation. We observed a similar effect of stress from waterlogging in all studied grassland types. The species composition of Puccinellia grasslands was the most stable over the three years with varying precipitation. This was important as this grassland type contained many threatened halophyte species. Self-organising maps revealed small-scale vegetation changes and provided a detailed visualisation of short-term vegetation dynamics, thus we suggest that the application of this method is also promising to reveal community dynamics in more species-rich habitat types or landscapes.
Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy.
Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma.
Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis.
Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.
Operating in a reverberating regime enables rapid tuning of network states to task requirements
(2018)
Neural circuits are able to perform computations under very diverse conditions and requirements. The required computations impose clear constraints on their fine-tuning: a rapid and maximally informative response to stimuli in general requires decorrelated baseline neural activity. Such network dynamics is known as asynchronous-irregular. In contrast, spatio-temporal integration of information requires maintenance and transfer of stimulus information over extended time periods. This can be realized at criticality, a phase transition where correlations, sensitivity and integration time diverge. Being able to flexibly switch, or even combine the above properties in a task-dependent manner would present a clear functional advantage. We propose that cortex operates in a "reverberating regime" because it is particularly favorable for ready adaptation of computational properties to context and task. This reverberating regime enables cortical networks to interpolate between the asynchronous-irregular and the critical state by small changes in effective synaptic strength or excitation-inhibition ratio. These changes directly adapt computational properties, including sensitivity, amplification, integration time and correlation length within the local network. We review recent converging evidence that cortex in vivo operates in the reverberating regime, and that various cortical areas have adapted their integration times to processing requirements. In addition, we propose that neuromodulation enables a fine-tuning of the network, so that local circuits can either decorrelate or integrate, and quench or maintain their input depending on task. We argue that this task-dependent tuning, which we call "dynamic adaptive computation," presents a central organization principle of cortical networks and discuss first experimental evidence.