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The Taklamakan and Gobi Desert (TGD) region has experienced a pronounced increase in summer precipitation, including high-impact extreme events, over recent decades. Despite identifying large-scale circulation changes as a key driver of the wetting trend, understanding the relative contributions of internal variability and external forcings remains limited. Here, we approach this problem by using a hierarchy of numerical simulations, complemented by diverse statistical analysis tools. Our results offer strong evidence that the atmospheric internal variations primarily drive this observed trend. Specifically, recent changes in the North Atlantic Oscillation have redirected the storm track, leading to increased extratropical storms entering TGD and subsequently more precipitation. A clustering analysis further demonstrates that these linkages predominantly operate at the synoptic scale, with larger contributions from large precipitation events. Our analysis highlights the crucial role of internal variability, in addition to anthropogenic forcing, when seeking a comprehensive understanding of future precipitation trends in TGD. The Taklamakan and Gobi Desert region has witnessed a significant rise in summer precipitation in recent decades. This study shows that atmospheric internal variability, rather than external forcings, is the main driver shaping this wetting trend. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a resource-efficient FPGA-based neural network regression model for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned ATLAS upgrades for the High Luminosity LHC, an entirely new FPGA-based hardware muon trigger system will be installed that will process full muon detector data within a 10 μ s latency window. The large FPGA devices planned for this upgrade should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our neural network regression model promises to improve rejection of the dominant source of background trigger events in the central detector region, which are due to muon candidates with low transverse momenta. This model was implemented in FPGA using 157 digital signal processors and about 5000 lookup tables. The simulated network latency and deadtime are 122 and 25 ns, respectively, when implemented in the FPGA device using a 320 MHz clock frequency. Two other FPGA implementations were also developed to study the impact of design choices on resource utilisation and latency. The performance parameters of our FPGA implementation are well within the requirements of the future muon trigger system, therefore opening a possibility for deploying machine learning methods for future data taking by the ATLAS experiment. [ABSTRACT FROM AUTHOR]

The family of voltage-gated potassium Kv2 channels consists of the Kv2.1 and Kv2.2 subtypes. Kv2.1 is constitutively highly phosphorylated in neurons and its function relies on its phosphorylation state. Whether the function of Kv2.2 is also dependent on its phosphorylation state remains unknown. Here, we investigated whether Kv2.2 channels can be phosphorylated by protein kinase C (PKC) and examined the effects of PKC-induced phosphorylation on their activity and function. Activation of PKC inhibited Kv2.2 currents and altered their steady-state activation in HEK293 cells. Point mutations and specific antibodies against phosphorylated S481 or S488 demonstrated the importance of these residues for the PKC-dependent modulation of Kv2.2. In layer II pyramidal neurons in cortical slices, activation of PKC similarly regulated native Kv2.2 channels and simultaneously reduced the frequency of action potentials. In conclusion, this study provides the first evidence to our knowledge that PKC-induced phosphorylation of the Kv2.2 channel controls the excitability of cortical pyramidal neurons. [ABSTRACT FROM AUTHOR]