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Fluctuations in host cell growth pose a critical challenge for maintaining reliable function in synthetic gene circuits. Growth-mediated dilution causes a global reduction in circuit component concentrations, which can significantly destabilize circuit behavior. However, effective strategies to counteract this problem remain lacking. Here, we present a phase-separation-based strategy to directly mitigate dilution effects. By fusing transcription factors (TFs) to intrinsically disordered regions (IDRs), we drive the formation of transcriptional condensates that concentrate TFs at their target promoters. These condensates buffer against prolonged rapid dilution of TF concentration and preserve bistable memory in self-activation circuits across variable growth conditions. We further show that this approach improves production efficiency in a cinnamic acid biosynthesis pathway. Together, our results establish liquid-liquid phase separation as an emerging design principle for constructing resilient synthetic circuits that maintain robust performance under dynamic growth conditions. 

Green initiatives are popular mechanisms globally to enhance environmental and human wellbeing. However, multiple green initiatives, when overlapping geographically and targeting the same participants, may interact with each other, giving rise to what is termed “spillover effects”, where one initiative and its outcomes influence another. This study examines the spillover effects among four major concurrent initiatives in the United States (U.S.) and China using a comprehensive dataset. In the U.S., we analysed county-level data in 2018 for the Conservation Reserve Program (CRP) and the Environmental Quality Incentives Program (EQIP), both operational for over 25 years. In China, data from Fanjingshan and Tianma National Nature Reserves (2014–2015) were used to evaluate the Grain-to-Green Program (GTGP) and the Forest Ecological Benefit Compensation (FEBC) program. The dataset comprises 3106 records for the U.S. and 711 plots for China, including several socio-economic variables. The results of multivariate linear regression indicate that there exist significant spillover effects between CRP & EQIP and GTGP & FEBC, with one initiative potentially enhancing or offsetting another’s impacts by 22% to 100%. This dataset provides valuable insights for researchers and policymakers to optimize the effectiveness and resilience of concurrent green initiatives. 

Abstract Over the past decade, topological insulators have received enormous attention for their potential in energy‐efficient spin‐to‐charge conversion, enabled by strong spin‐orbit coupling and spin‐momentum locked surface states. Despite extensive research, the spin‐to‐charge conversion efficiency, usually characterized by the spin Hall angle (θ_SH), remains relatively low at room temperature. In this work, pulsed laser deposition is employed to fabricate high‐quality ternary topological insulator (Bi_0.1Sb_0.9)₂Te₃thin films on magnetic insulator Y₃Fe₅O₁₂. It is found that the value ofθ_SHreaches ≈0.76 at room temperature and increases to ≈0.9 as the Fermi level is tuned to cross topological surface states via electrical gating. These findings provide an innovative approach to tailoring the spin‐to‐charge conversion in topological insulators and pave the way for their applications in energy‐efficient spintronic devices. 

Recent band structure calculations have suggested the potential for band tuning in the chiral semiconductor Ag3AuTe2 to zero upon application of negative strain. In this study, we report on the synthesis of polycrystalline Ag3AuTe2 and investigate its transport and optical properties and mechanical compressibility. Transport measurements reveal the semiconducting behavior of Ag3AuTe2 with high resistivity and an activation energy Ea of 0.2 eV. The optical bandgap determined by diffuse reflectance measurements is about three times wider than the experimental Ea. Despite the difference, both experimental gaps fall within the range of predicted bandgaps by our first-principles density functional theory (DFT) calculations employing the Perdew–Burke–Ernzerhof and modified Becke–Johnson methods. Furthermore, our DFT simulations predict a progressive narrowing of the bandgap under compressive strain, with a full closure expected at a strain of −4% relative to the lattice parameter. To evaluate the feasibility of gap tunability at such substantial strain, the high-pressure behavior of Ag3AuTe2 was investigated by in situ high-pressure x-ray diffraction up to 47 GPa. Mechanical compression beyond 4% resulted in a pressure-induced structural transformation, indicating the possibility of substantial gap modulation under extreme compression conditions. 

Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications. 

Abstract The field of synthetic biology and biosystems engineering increasingly acknowledges the need for a holistic design approach that incorporates circuit-host interactions into the design process. Engineered circuits are not isolated entities but inherently entwined with the dynamic host environment. One such circuit-host interaction, ‘growth feedback’, results when modifications in host growth patterns influence the operation of gene circuits. The growth-mediated effects can range from growth-dependent elevation in protein/mRNA dilution rate to changes in resource reallocation within the cell, which can lead to complete functional collapse in complex circuits. To achieve robust circuit performance, synthetic biologists employ a variety of control mechanisms to stabilize and insulate circuit behavior against growth changes. Here we propose a simple strategy by incorporating one repressive edge in a growth-sensitive bistable circuit. Through both simulation and in vitro experimentation, we demonstrate how this additional repressive node stabilizes protein levels and increases the robustness of a bistable circuit in response to growth feedback. We propose the incorporation of repressive links in gene circuits as a control strategy for desensitizing gene circuits against growth fluctuations. 

Abstract Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remain challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counterparts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods’ properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous morphological characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our method’s behavior, and suggest future research venues. 

Abstract Convergent cross-mapping (CCM) has attracted increased attention recently due to its capability to detect causality in nonseparable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure DI flow? Here, we first causalize CCM so that it aligns with the presumption in causality analysis—the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and DI under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate DI and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural networks and other settings, either random or deterministic, or both.