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1. modSaRa: a computationally efficient R package for CNV identification

   https://doi.org/10.1093/bioinformatics/btx212  

   Xiao, Feifei; Niu, Yue; Hao, Ning; Xu, Yanxun; Jin, Zhilin; Zhang, Heping (August 2017, Bioinformatics)

   Hancock, John (Ed.)

   Abstract SummaryChromosomal copy number variation (CNV) refers to a polymorphism that a DNA segment presents deletion or duplication in the population. The computational algorithms developed to identify this type of variation are usually of high computational complexity. Here we present a user-friendly R package, modSaRa, designed to perform copy number variants identification. The package is developed based on a change-point based method with optimal computational complexity and desirable accuracy. The current version of modSaRa package is a comprehensive tool with integration of preprocessing steps and main CNV calling steps. Availability and ImplementationmodSaRa is an R package written in R, C ++ and Rcpp and is now freely available for download at http://c2s2.yale.edu/software/modSaRa. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

   https://doi.org/10.1126/sciadv.abf8124  

   Rozum, Jordan C.; Gómez Tejeda Zañudo, Jorge; Gan, Xiao; Deritei, Dávid; Albert, Réka (July 2021, Science Advances)

   We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size ( N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system’s relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors. 

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3. Evolution of complexity and the transition to biochemical life

   https://doi.org/10.1103/PhysRevE.111.064403  

   Gagrani, Praful; Baum, David (June 2025, Physical Review E)

   While modern physics and biology satisfactorily explain the passage from the Big Bang to the formation of Earth and the first cells to present-day life, respectively, the origins of biochemical life still remain an open question. Since life, as we know it, requires extremely long genetic polymers, any answer to the question must explain how an evolving system of polymers of ever-increasing length could come about on a planet that otherwise consisted only of small molecular building blocks. In this work we show that, under realistic constraints, an abstract polymer model can exhibit dynamics such that attractors in the polymer population space with a higher average polymer length are also more probable. We generalize from the model and formalize the notions of and for chemical reaction networks with multiple attractors. The complexity of a species is defined as the minimum number of reactions needed to produce it from a set of building blocks, which in turn is used to define a measure of complexity for an attractor. A transition between attractors is considered to be a if the attractor with the higher probability also has a higher complexity. In an environment where only monomers are readily available, the attractor with a higher average polymer length is more complex. Thus, by this criterion, our abstract polymer model can exhibit progressive evolution for a range of thermodynamically plausible rate constants. We also formalize criteria for and evolution and explain the role of autocatalysis in obtaining them. Our work provides a basis for searching for prebiotically plausible scenarios in which long polymers can emerge and yield populations with even longer polymers. Additionally, the existence of features like history dependence and open endedness support the view that the path from chemistry to biology was one of gradual complexification rather than an instantaneous origin of life. Published by the American Physical Society2025 

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4. Dilaton-axion inflation with PBHs and GWs

   https://doi.org/10.1088/1475-7516/2022/08/037  

   Kallosh, Renata; Linde, Andrei (August 2022, Journal of Cosmology and Astroparticle Physics)

   Abstract We discuss two-stage dilaton-axion inflation models [1] and describe α -attractor models with either exponential or polynomial approach to the plateau.We implement one of the models of primordial black hole production proposed in [2] in the α -attractor context, and develop its supergravity version. The predictions of this model following from its polynomial attractor properties are: n s and r are α -independent, r depends on the mass parameter μ defining the approach to the plateau. The tachyonic instability at the transition point between the two stages of inflation is proportional to the negative curvature of the hyperbolic space ℛ K = -2/3 α . Thereforethe masses of primordial black holes (PBHs) and the frequencies of small-scale gravitational waves (GWs) in this model show significant dependence on  α . 

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5. A Computationally Efficient, High-Fidelity Testbed for Building Climate Control

   https://doi.org/10.1115/1.4048895  

   Kircher, Kevin J.; Schaefer, Walter; Max Zhang, K. (February 2021, ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract Advanced building climate control systems have the potential to significantly reduce greenhouse gas emissions and energy costs, but more research is needed to bring these systems to market. A key component of building control research is testing algorithms through simulation. Many high-fidelity simulation testbeds exist, but they tend to be complex and opaque to users. Simpler, more transparent testbeds also exist, but they tend to neglect important nonlinearities and disturbances encountered in practice. In this paper, we develop a simulation testbed that is computationally efficient, transparent and high fidelity. We validate the testbed empirically, then demonstrate its use through the examples of system identification, online state and parameter estimation, and model predictive control (MPC). The testbed is intended to enable rapid, reliable analysis of building control algorithms, thereby accelerating progress toward reducing greenhouse gas emissions at scale. We call the resulting testbed and supporting functions the bldg toolbox, which is free, open source, and available online. 

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