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1. Cycle-by-cycle analysis of neural oscillations

   https://doi.org/10.1152/jn.00273.2019  

   Cole, Scott; Voytek, Bradley (July 2019, Journal of Neurophysiology)

   Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. This has successfully uncovered numerous links between oscillations and cognition or disease. However, neural data are nonsinusoidal, and these nonsinusoidal features are increasingly linked to a variety of behavioral and cognitive states, pathophysiology, and underlying neuronal circuit properties. Here, we present a new analysis framework-one that is complementary to existing Fourier- and Hilbert-transform based approaches-that quantifies oscillatory features in the time domain, on a cycle-by-cycle basis. We have released this cycle-by-cycle analysis suite as bycycle, a fully documented, open-source Python package with detailed tutorials and troubleshooting cases. This approach performs tests to assess whether an oscillation is present at any given moment and, if so, quantifies each oscillatory cycle by its amplitude, period, and waveform symmetry, the latter of which is missed using conventional approaches. In a series of simulated event-related studies, we show how conventional Fourier- and Hilbert-transform approaches can conflate event-related changes in oscillation burst duration as increased oscillatory amplitude and as a change in the oscillation frequency, even though those features were unchanged in simulation. Our approach avoids these errors. Further, we validate this approach in simulation and against experimental recordings of patients with Parkinson's disease, who are known to have nonsinusoidal beta (12-30 Hz) oscillations. 

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2. Incremental Cycle Bases for Cycle-Based Pose Graph Optimization

   https://doi.org/10.1109/LRA.2023.3236580  

   Forsgren, Brendon; Brink, Kevin; Ganesh, Prashant; McLain, Timothy W. (February 2023, IEEE Robotics and Automation Letters)
3. Cycle-to-Cycle Variation Suppression in ReRAM-Based AI Accelerators

   https://doi.org/10.1109/PAINE58317.2023.10317995  

   Fu, Jingyan; Liao, Zhiheng; Wang, Jinhui (October 2023, Proc. IEEE International Conference on Physical Assurance and Inspection of Electronics (PAINE'23))
4. Business-Cycle Anatomy

   https://doi.org/10.1257/aer.20181174  

   Angeletos, George-Marios; Collard, Fabrice; Dellas, Harris (October 2020, American Economic Review)

   null (Ed.)

   We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mechanism that best describes the data, and use its properties to appraise models of both the parsimonious and the medium-scale variety. Our findings support the existence of a main business-cycle driver but rule out the following candidates for this role: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type. Models aimed at accommodating demand-driven cycles without a strict reliance on nominal rigidity appear promising. (JEL C22, E10, E32) 

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5. Photorespiration: The Futile Cycle?

   https://doi.org/10.3390/plants10050908  

   Shi, Xiaoxiao; Bloom, Arnold (May 2021, Plants)

   null (Ed.)

   Photorespiration, or C2 photosynthesis, is generally considered a futile cycle that potentially decreases photosynthetic carbon fixation by more than 25%. Nonetheless, many essential processes, such as nitrogen assimilation, C1 metabolism, and sulfur assimilation, depend on photorespiration. Most studies of photosynthetic and photorespiratory reactions are conducted with magnesium as the sole metal cofactor despite many of the enzymes involved in these reactions readily associating with manganese. Indeed, when manganese is present, the energy efficiency of these reactions may improve. This review summarizes some commonly used methods to quantify photorespiration, outlines the influence of metal cofactors on photorespiratory enzymes, and discusses why photorespiration may not be as wasteful as previously believed. 

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