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1. A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas

   https://doi.org/10.1088/2632-2153/abe7b7  

   Ding, Zhiyue; Matthews, Lorin S; Hyde, Truell W (June 2021, Machine Learning: Science and Technology)

   Abstract Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves. 

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2. Heterogeneous response intensity ranges and response probability improve goal achievement in multi-agent systems

   Mathias, H. David; Wu, Annie S.; Ruetten, Laik (January 2020, Proceedings of the 12th International Conference on Swarm Intelligence)

   null (Ed.)

   Inter-agent variation is well-known in both the biology and computer science communities as a mechanism for improving task selection and swarm performance for multi-agent systems. Response threshold variation, the most commonly used form of inter-agent variation, desynchronizes agent actions allowing for more targeted agent activation. Recent research using a less common form of variation, termed dynamic response intensity, demonstrates that modeling levels of agent experience or varying physical attributes and using these to allow some agents to perform tasks more efficiently or vigorously, significantly improves swarm goal achievement when used in conjunction with response thresholds. Dynamic intensity values vary within a fixed range as agents activate for tasks. We extend previous work by demonstrating that adding another layer of variation to response intensity, in the form of heterogeneous ranges for response intensity values, provides significant performance improvements when response is probabilistic. Heterogeneous intensity ranges break the coupling that occurs between response thresh- olds and response intensities when the intensity range is homogeneous. The decoupling allows for increased diversity in agent behavior. 

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3. A Pathway for Northern Hemisphere Extratropical Cooling to Elicit a Tropical Response

   https://doi.org/10.1029/2022GL100719  

   Luongo, Matthew_T; Xie, Shang‐Ping; Eisenman, Ian; Hwang, Yen‐Ting; Tseng, Hung‐Yi (January 2023, Geophysical Research Letters)

   Abstract Previous studies have found that Northern Hemisphere aerosol‐like cooling induces a La Niña‐like response in the tropical Indo‐Pacific. Here, we explore how a coupled ocean‐atmosphere feedback pathway communicates and sustains this response. We override ocean surface wind stress in a comprehensive climate model to decompose the total ocean‐atmosphere response to forced extratropical cooling into the response of surface buoyancy forcing alone and surface momentum forcing alone. In the subtropics, the buoyancy‐forced response dominates: the positive low cloud feedback amplifies sea surface temperature (SST) anomalies which wind‐driven evaporative cooling communicates to the tropics. In the equatorial Indo‐Pacific, buoyancy‐forced ocean dynamics cool the surface while the Bjerknes feedback creates zonally asymmetric SST patterns. Although subtropical cloud feedbacks are model‐dependent, our results suggest this feedback pathway is robust across a suite of models such that models with a stronger subtropical low cloud response exhibit a stronger La Niña response. 

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4. Codon-Dependent Transcriptional Changes in Response to Tryptophan Limitation in the Tryptophan Auxotrophic Pathogens Chlamydia trachomatis and Streptococcus pyogenes

   https://doi.org/10.1128/mSystems.01269-21  

   Ouellette, Scot P.; Hatch, Nathan D.; Wood, Nicholas A.; Herrera, Andrea L.; Chaussee, Michael S. (December 2021, mSystems)

   van Wezel, Gilles P. (Ed.)

   ABSTRACT Chlamydia trachomatis and Streptococcus pyogenes are among the most prevalent bacterial pathogens of humans. Interestingly, both pathogens are tryptophan (Trp) auxotrophs and must acquire this essential amino acid from their environment. For Chlamydia , an obligate intracellular bacterium, this means scavenging Trp from the host cell in which they reside. For Streptococcus , a primarily extracellular bacterium, this means scavenging Trp from the local environment. In the course of a natural immune response, both pathogens can be exposed to Trp-limiting conditions through the action of the interferon gamma-inducible IDO1 enzyme, which catabolizes Trp to N -formylkynurenine. How these pathogens respond to Trp starvation is incompletely understood. However, we have previously demonstrated that genes enriched in Trp codons were preferentially transcribed in C. pneumoniae during Trp limitation. Chlamydia , but not Streptococcus , lacks a stringent response, which is a global regulon activated by uncharged tRNAs binding in the A site of the ribosome. We hypothesized that the chlamydial response to Trp limitation is a consequence of lacking a stringent response. To test this, we compared global transcription profiles of C. trachomatis to both wild-type and stringent response mutant strains of Streptococcus during Trp starvation. We observed that both Trp auxotrophs respond with codon-dependent changes in their transcriptional profiles that correlate with Trp codon content but not transcript stability. Importantly, the stringent response had no impact on these transcriptional changes, suggesting an evolutionarily conserved adaptation to Trp starvation. Therefore, we have revealed a novel response of Trp auxotrophic pathogens in response to Trp starvation. IMPORTANCE Chlamydia trachomatis and Streptococcus pyogenes are important pathogens of humans. Interestingly, both are auxotrophic for tryptophan and acquire this essential amino acid from the host environment. However, part of the host defense against pathogens includes the degradation of tryptophan pools. Therefore, Chlamydia and Streptococcus are particularly susceptible to tryptophan starvation. Most model bacteria respond to amino acid starvation by using a global regulon called the stringent response. However, Chlamydia lacks a stringent response. Here, we investigated the chlamydial response to tryptophan starvation and compared it to both wild-type and stringent response mutant strains of S. pyogenes to determine what role a functional stringent response plays during tryptophan starvation in these pathogens. We determined that both of these pathogens respond to tryptophan starvation by increasing transcription of tryptophan codon-rich genes. This effect was not dependent on the stringent response and highlights a previously unrecognized and potentially evolutionarily conserved mechanism for surviving tryptophan starvation. 

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5. Precise Traits from Sloppy Components: Perception and the Origin of Phenotypic Response

   https://doi.org/10.3390/e25081162  

   Frank, Steven A. (August 2023, Entropy)

   Organisms perceive their environment and respond. The origin of perception–response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception–response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection. 

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