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Abstract Atmospheric turbulence plays a key role in the mixing of trace gases and diffusion of heat and momentum, as well as in aircraft operations. Although numerous observational turbulence studies have been conducted using campaign experiments and operational data, understanding the turbulence characteristics particularly in the free atmosphere remains challenging due to its small-scale, intermittent, and sporadic nature, along with limited observational data. To address this, turbulence in the free atmosphere is estimated herein based on the Thorpe method by using operational high vertical-resolution radiosonde data (HVRRD) with vertical resolutions of about 5 or 10 m across near-global regions, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) via the U.S. National Centers for Environmental Information (NCEI) for 6 years (October 2017–September 2023). Globally, turbulence is stronger in the troposphere than in the stratosphere, with maximum turbulence occurring about 6 km below the tropopause, followed by a sharp decrease above. Seasonal variations show strong tropospheric turbulence in summer and weak turbulence in winter for both hemispheres, while the stratosphere exhibits strong turbulence during spring. Regional analyses identify strong turbulence regions over the South Pacific and South Africa in the troposphere and over East Asia and South Africa in the stratosphere. Notably, turbulence information can be provided in regions and high altitudes that are not covered by commercial aircraft, suggesting its potential utility for both present and future high-altitude aircraft operations. 

Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels deep. In several applications, including biological workflows, chemical equations, and computational data flow analysis, these graphs often exhibit a tree-like nesting structure, where sibling clusters are disjoint. Common compound graph layouts prioritize the lowest level of the grouping, down to the individual ungrouped vertices, which can make the higher level grouped structures more difficult to discern, especially in deeply nested networks. Leveraging the additional structure of the tree-like nesting, we contribute an overview+detail layout for this class of compound graphs that preserves the saliency of the higher level network structure when groups are expanded to show internal nested structure. Our layout draws inner structures adjacent to their parents, using a modified tree layout to place substructures. We describe our algorithm and then present case studies demonstrating the layout's utility to a domain expert working on data flow analysis. Finally, we discuss network parameters and analysis situations in which our layout is well suited. 

Shotgun proteomics has been widely used to identify histone marks. Conventional database search methods rely on the “target-decoy” strategy to calculate the false discovery rate (FDR) and distinguish true peptide-spectrum matches (PSMs) from false ones. This strategy has a caveat of inaccurate FDR caused by the small data size of histone marks. To address this challenge, we developed a tailored database search strategy, named “Comprehensive Histone Mark Analysis (CHiMA).” Instead of target-decoy–based FDR, this method uses “50% matched fragment ions” as the key criterion to identify high-confidence PSMs. CHiMA identified twice as many histone modification sites as the conventional method in benchmark datasets. Reanalysis of our previous proteomics data using CHiMA led to the identification of 113 new histone marks for four types of lysine acylations, almost doubling the number of previously reported marks. This tool not only offers a valuable approach for identifying histone modifications but also greatly expands the repertoire of histone marks. 

Motivated by regulating/eliminating the population of herbivorous pests, we investigate a discrete-time plant–herbivore model with two different constant control strategies (removal versus reduction), and formulate the corresponding optimal control problems when its dynamics exhibits varied types of bi-stability and fluctuating environments. We provide basic analysis and identify the critical factors to characterize the optimal controls and the corresponding plant–herbivore dynamics such as the control upper bound (the effectiveness level of the implementation of control measures) and the initial conditions of the plant and herbivore. Our results show that optimal control could be easier when the model has simple dynamics such as stable equilibrium dynamics under constant environment or the model exhibits chaotic dynamics under fluctuating environments. Due to bistability, initial conditions are important for optimal controls. Regardless of with or without fluctuating environments, initial conditions taken from the near the boundary makes optimal control easier. In general, the pest is hard to be eliminated when the control upper bound is not large enough. However, as the control upper bound is increased or the initial conditions are chosen from near the boundary of the basin of attractions, the pest can be manageable regardless of the fluctuating environments.