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1. Aerosol Influences on Cloud Water: Insights From ARM EPCAPE Observations With Explainable Machine Learning

   https://doi.org/10.1029/2025GL115163  

   Zhang, Haipeng; Zhang, Yunyan; Li, Zhanqing; Zheng, Youtong (August 2025, Geophysical Research Letters)

   Abstract This study employs an explainable machine learning (ML) framework (XGBoost‐SHapley Additive exPlanations analysis) to investigate controlling factors on cloud liquid water path (LWP) using EPCAPE observations near the California coast. Aerosols are found to be the dominant factor explaining LWP variability, surpassing meteorological factors (MFs). By isolating aerosol effects from meteorological influences, the ML reveals a negative linear relationship between LWP and cloud droplet number concentration (N_d) in log space, likely driven by entrainment drying via evaporation‐entrainment feedback. This aligns with the negative regime of the inverted‐V relationship reported in previous studies, while no positive LWP responses are found due to a limited number of precipitating cases in EPCAPE. Furthermore, the sensitivity of LWP toN_dshows a non‐linear dependence on MFs like moisture contrast between surface and free troposphere and lower‐tropospheric stability. This occurs due to the interplay between the MFs' direct effects on entrainment drying and indirect effects through LWP adjustments. 

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2. Arm-in-Arm Response Regulator Dimers Promote Intermolecular Signal Transduction

   https://doi.org/10.1128/JB.00872-15  

   Baker, Anna W.; Satyshur, Kenneth A.; Moreno Morales, Neydis; Forest, Katrina T.; Stock, A. M. (March 2016, Journal of Bacteriology)

   ABSTRACT Bacteriophytochrome photoreceptors (BphPs) and their cognate response regulators make up two-component signal transduction systems which direct bacteria to mount phenotypic responses to changes in environmental light quality. Most of these systems utilize single-domain response regulators to transduce signals through unknown pathways and mechanisms. Here we describe the photocycle and autophosphorylation kinetics of RtBphP1, a red light-regulated histidine kinase from the desert bacterium Ramlibacter tataouinensis . RtBphP1 undergoes red to far-red photoconversion with rapid thermal reversion to the dark state. RtBphP1 is autophosphorylated in the dark; this activity is inhibited under red light. The RtBphP1 cognate response regulator, the R. tataouinensis bacteriophytochrome response regulator (RtBRR), and a homolog, AtBRR from Agrobacterium tumefaciens , crystallize unexpectedly as arm-in-arm dimers, reliant on a conserved hydrophobic motif, hFWAhL (where h is a hydrophobic M, V, L, or I residue). RtBRR and AtBRR dimerize distinctly from four structurally characterized phytochrome response regulators found in photosynthetic organisms and from all other receiver domain homodimers in the Protein Data Bank. A unique cacodylate-zinc-histidine tag metal organic framework yielded single-wavelength anomalous diffraction phases and may be of general interest. Examination of the effect of the BRR stoichiometry on signal transduction showed that phosphorylated RtBRR is accumulated more efficiently than the engineered monomeric RtBRR (RtBRR mon ) in phosphotransfer reactions. Thus, we conclude that arm-in-arm dimers are a relevant signaling intermediate in this class of two-component regulatory systems. IMPORTANCE BphP histidine kinases and their cognate response regulators comprise widespread red light-sensing two-component systems. Much work on BphPs has focused on structural understanding of light sensing and on enhancing the natural infrared fluorescence of these proteins, rather than on signal transduction or the resultant phenotypes. To begin to address this knowledge gap, we solved the crystal structures of two single-domain response regulators encoded by a region immediately downstream of that encoding BphPs. We observed a previously unknown arm-in-arm dimer linkage. Monomerization via deletion of the C-terminal dimerization motif had an inhibitory effect on net response regulator phosphorylation, underlining the importance of these unusual dimers for signal transduction. 

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3. Top Feasible Arm Identification

   Katz-Samuels, Julian; Scott, Clayton (January 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS))

   We propose a new variant of the top arm identification problem, top feasible arm identification, where there are K arms associated with D-dimensional distributions and the goal is to find m arms that maximize some known linear function of their means subject to the constraint that their means belong to a given set P € R D. This problem has many applications since in many settings, feedback is multi-dimensional and it is of interest to perform constrained maximization. We present problem-dependent lower bounds for top feasible arm identification and upper bounds for several algorithms. Our most broadly applicable algorithm, TF-LUCB-B, has an upper bound that is loose by a factor of OpDlogpKqq. Many problems of practical interest are two dimensional and, for these, it is loose by a factor of OplogpKqq. Finally, we conduct experiments on synthetic and real-world datasets that demonstrate the effectiveness of our algorithms. Our algorithms are superior both in theory and in practice to a naive two-stage algorithm that first identifies the feasible arms and then applies a best arm identification algorithm to the feasible arms. 

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4. Robust Outlier Arm Identification

   Zhu, Yinglun; Katariya, Sumeet; Nowak, Robert (July 2020, International Conference on Machine Learning)

   We study the problem of Robust Outlier Arm Identification (ROAI), where the goal is to identify arms whose expected rewards deviate substantially from the majority, by adaptively sampling from their reward distributions. We compute the outlier threshold using the median and median absolute deviation of the expected rewards. This is a robust choice for the threshold compared to using the mean and standard deviation, since it can identify outlier arms even in the presence of extreme outlier values. Our setting is different from existing pure exploration problems where the threshold is pre-specified as a given value or rank. This is useful in applications where the goal is to identify the set of promising items but the cardinality of this set is unknown, such as finding promising drugs for a new disease or identifying items favored by a population. We propose two 𝛿 -PAC algorithms for ROAI, which includes the first UCB-style algorithm for outlier detection, and derive upper bounds on their sample complexity. We also prove a matching, up to logarithmic factors, worst case lower bound for the problem, indicating that our upper bounds are generally unimprovable. Experimental results show that our algorithms are both robust and about 5 x sample efficient compared to state-of-the-art. 

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5. Investigation of aerosol–cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) southern Great Plains (SGP) ground-based measurements

   https://doi.org/10.5194/acp-20-3483-2020  

   Zheng, Xiaojian; Xi, Baike; Dong, Xiquan; Logan, Timothy; Wang, Yuan; Wu, Peng (January 2020, Atmospheric Chemistry and Physics)

   null (Ed.)

   Abstract. The aerosol indirect effect on cloud microphysical and radiative propertiesis one of the largest uncertainties in climate simulations. In order toinvestigate the aerosol–cloud interactions, a total of 16 low-level stratuscloud cases under daytime coupled boundary-layer conditions are selectedover the southern Great Plains (SGP) region of the United States. Thephysicochemical properties of aerosols and their impacts on cloudmicrophysical properties are examined using data collected from theDepartment of Energy Atmospheric Radiation Measurement (ARM) facility at the SGP site. The aerosol–cloud interaction index (ACIr) is used to quantify the aerosol impacts with respect to cloud-droplet effective radius. The mean value of ACIr calculated from all selected samples is0.145±0.05 and ranges from 0.09 to 0.24 at a range of cloudliquid water paths (LWPs; LWP=20–300 g m−2). The magnitude of ACIr decreases with an increasing LWP, which suggests a diminished cloud microphysical response to aerosol loading, presumably due to enhanced condensational growth processes and enlarged particle sizes. The impact of aerosols with different light-absorbing abilities on the sensitivity of cloud microphysical responses is also investigated. In the presence of weak light-absorbing aerosols, the low-level clouds feature a higher number concentration of cloud condensation nuclei (NCCN) and smaller effective radii (re), while the opposite is true for strong light-absorbing aerosols. Furthermore, the mean activation ratio of aerosols to CCN (NCCN∕Na) for weakly (strongly) absorbing aerosols is 0.54 (0.45), owing to the aerosol microphysical effects, particularly the different aerosol compositions inferred by their absorptive properties. In terms of the sensitivity of cloud-droplet number concentration (Nd) to NCCN, the fraction of CCN that converted to cloud droplets (Nd∕NCCN) for the weakly (strongly) absorptive regime is 0.69 (0.54). The measured ACIr values in the weakly absorptive regime arerelatively higher, indicating that clouds have greater microphysicalresponses to aerosols, owing to the favorable thermodynamic condition. Thereduced ACIr values in the strongly absorptive regime are due to the cloud-layer heating effect induced by strong light-absorbing aerosols. Consequently, we expect larger shortwave radiative cooling effects from clouds in the weakly absorptive regime than those in the strongly absorptive regime. 

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