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1. Zonally asymmetric component of summer surface temperature trends caused by intraseasonal time-scale processes

   https://doi.org/10.1038/s41612-023-00522-z  

   Kim, Dong Wan; Lee, Sukyoung; Clark, Joseph P; Feldstein, Steven B (December 2023, npj Climate and Atmospheric Science)

   Abstract Recent years have witnessed extreme heatwaves in Europe and western North America. This study shows that these regions stand out in the zonally asymmetric component of the long-term trend of boreal summer surface temperature, and that intraseasonal timescale processes play an important role in shaping the zonally asymmetric trend pattern. However, these two regions have warmed by different mechanisms. Over Europe, the warming is mostly caused by the positive trend of the net (downward minus upward) surface shortwave radiation weighted by its intraseasonal timescale connection with the skin temperature. The long-term warming in western North America has been caused by the declining surface latent heat flux (weakened evaporative cooling) weighted by its intraseasonal connection with the skin temperature. These mechanisms are consistent with those identified in earlier studies of individual extreme events in the two regions, indicating that part of the long trends are a manifestation of extreme events. The overall findings indicate that to make accurate projections of regional climate change using climate model simulations, it is critical to ensure that the models also accurately simulate intraseasonal variability. 

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2. Sea Surface Salinity Change since 1950: Internal Variability versus Anthropogenic Forcing

   https://doi.org/10.1175/JCLI-D-20-0331.1  

   Sun, Qiwei; Du, Yan; Xie, Shang-Ping; Zhang, Yuhong; Wang, Minyang; Kosaka, Yu (February 2021, Journal of Climate)

   null (Ed.)

   Abstract Using an eastern tropical Pacific pacemaker experiment called the Pacific Ocean–Global Atmosphere (POGA) run, this study investigated the internal variability in sea surface salinity (SSS) and its impacts on the assessment of long-term trends. By constraining the eastern tropical Pacific sea surface temperature variability with observations, the POGA experiment successfully simulated the observed variability of SSS. The long-term trend in POGA SSS shows a general pattern of salty regions becoming saltier (e.g., the northern Atlantic) and fresh regions becoming fresher, which agrees with previous studies. The 1950–2012 long-term trend in SSS is modulated by the internal variability associated with the interdecadal Pacific oscillation (IPO). Due to this variability, there are some regional discrepancies in the SSS 1950–2012 long-term change between POGA and the free-running simulation forced with historical radiative forcing, especially for the western tropical Pacific and southeastern Indian Ocean. Our analysis shows that the tropical Pacific cooling and intensified Walker circulation caused the SSS to increase in the western tropical Pacific and decrease in the southeastern Indian Ocean during the 20-yr period of 1993–2012. This decadal variability has led to large uncertainties in the estimation of radiative-forced trends on a regional scale. For the 63-yr period of 1950–2012, the IPO caused an offset of ~40% in the radiative-forced SSS trend in the western tropical Pacific and ~170% enhancement in the trend in the southeastern Indian Ocean. Understanding and quantifying the contribution of internal variability to SSS trends helps improve the skill for estimates and prediction of salinity/water cycle changes. 

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3. An Ultra-Low Power Spintronic Stochastic Spiking Neuron with Self-Adaptive Discrete Sampling

   Sheikhfaal, Shadi; Pyle, Steven D.; Salehi, Soheil; DeMara, Ronald F. (August 2019, IEEE International Midwest Symposium on Circuits and Systems (MWSCAS))

   State-of-the-art machine learning models have achieved impressive feats of narrow intelligence, but have yet to realize the computational generality, adaptability, and power efficiency of biological brains. Thus, this work aims to improve current neural network models by leveraging the principle that the cortex consists of noisy and imprecise components in order to realize an ultra-low-power stochastic spiking neural circuit that resembles biological neuronal behavior. By utilizing probabilistic spintronics to provide true stochasticity in a compact CMOS-compatible device, an Adaptive Ring Oscillator for as-needed discrete sampling, and a homeostasis mechanism to reduce power consumption, provide additional biological characteristics, and improve process variation resilience, this subthreshold circuit is able to generate sub-nanosecond spiking behavior with biological characteristics at 200mV, using less than 80nW, along with behavioral robustness to process variation. 

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4. Circuit design in biology and machine learning. I. Random networks and dimensional reduction

   https://doi.org/10.1093/evolut/qpaf065  

   Frank, Steven A (May 2025, Evolution)

   Abstract A biological circuit is a neural or biochemical cascade, taking inputs and producing outputs. How have biological circuits learned to solve environmental challenges over the history of life? The answer certainly follows Dobzhansky’s famous quote that “nothing in biology makes sense except in the light of evolution.” But that quote leaves out the mechanistic basis by which natural selection’s trial-and-error learning happens, which is exactly what we have to understand. How does the learning process that designs biological circuits actually work? How much insight can we gain about the form and function of biological circuits by studying the processes that have made those circuits? Because life’s circuits must often solve the same problems as those faced by machine learning, such as environmental tracking, homeostatic control, dimensional reduction, or classification, we can begin by considering how machine learning designs computational circuits to solve problems. We can then ask: How much insight do those computational circuits provide about the design of biological circuits? How much does biology differ from computers in the particular circuit designs that it uses to solve problems? This article steps through two classic machine learning models to set the foundation for analyzing broad questions about the design of biological circuits. One insight is the surprising power of randomly connected networks. Another is the central role of internal models of the environment embedded within biological circuits, illustrated by a model of dimensional reduction and trend prediction. Overall, many challenges in biology have machine learning analogs, suggesting hypotheses about how biology’s circuits are designed. 

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5. Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA

   https://doi.org/10.1021/acssynbio.1c00159  

   Buecherl, Lukas; Roberts, Riley; Fontanarrosa, Pedro; Thomas, Payton J.; Mante, Jeanet; Zhang, Zhen; Myers, Chris J. (October 2021, ACS synthetic biology)

   In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit’s functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation. 

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