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Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and nonantagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains ofSaccharomyces cerevisiae, we show that “boom-and-bust” dynamics—periods of rapid growth punctuated by episodic mortality events—caused by temporal environmental fluctuations can favor nonantagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom-and-bust environments where population dilutions occur either deterministically or stochastically over time. Our mathematical investigation reveals that optimal toxin regulation is much more beneficial to killers in stochastic, rather than deterministic, boom-and-bust environments. Overall, our findings show how both antagonistic and nonantagonistic microbes can thrive under varying environmental conditions. 

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative “cost” of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a “budget”). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such “threshold-aware” optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new “threshold-aware” policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. 

Antagonistic interactions are critical determinants of microbial community stability and composition, offering host benefits such as pathogen protection and providing avenues for antimicrobial control. While the ability to eliminate competitors confers an advantage to antagonistic microbes, it often incurs a fitness cost. Consequently, many microbes only produce toxins or engage in antagonistic behavior in response to specific cues like quorum sensing molecules or environmental stress. In laboratory settings, antagonistic microbes typically dominate over sensitive ones, raising the question of why both antagonistic and non-antagonistic microbes are found in natural environments and host microbiomes. Here, using both theoretical models and experiments with killer strains ofSaccharomyces cerevisiae, we show that boom-and-bust dynamics caused by temporal environmental fluctuations can favor non-antagonistic microbes that do not incur the growth rate cost of toxin production. Additionally, using control theory, we derive bounds on the competitive performance and identify optimal regulatory toxin-production strategies in various boom- and-bust environments where population dilutions occur either deterministically or stochastically over time. Our findings offer a new perspective on how both antagonistic and non-antagonistic microbes can thrive under varying environmental conditions. 

Anthropogenic ammonia (NH₃) emissions have significantly increased in recent decades due to enhanced agricultural activities, contributing to global air pollution. While the effects of NH₃on surface air quality are well documented, its influence on particle dynamics in the upper troposphere-lower stratosphere (UTLS) and related aerosol impacts remain unquantified. NH₃reaches the UTLS through convective transport and can enhance new particle formation (NPF). This modeling study evaluates the global impact of anthropogenic NH₃on UTLS particle formation and quantifies its effects on aerosol loading and cloud condensation nuclei (CCN) abundance. We use the EMAC Earth system model, incorporating multicomponent NPF parameterizations from the CERN CLOUD experiment. Our simulations reveal that convective transport increases NH₃-driven NPF in the UTLS by one to three orders of magnitude compared to a baseline scenario without anthropogenic NH₃, causing a doubling of aerosol numbers over high-emission regions. These aerosol changes induce a 2.5-fold increase in upper tropospheric CCN concentrations. Anthropogenic NH₃emissions increase the relative contribution of water-soluble inorganic ions to the UTLS aerosol optical depth (AOD) by 20% and increase total column AOD by up to 80%. In simulations without anthropogenic NH₃, UTLS aerosol composition is dominated by sulfate and organic species, with a marked reduction in ammonium nitrate and aerosol water content. This results in a decline of aerosol mass concentration by up to 50%. These findings underscore the profound global influence of anthropogenic NH₃emissions on UTLS particle formation, AOD, and CCN production, with important implications for cloud formation and climate. 

Abstract Relativistic Weyl fermion quasiparticles in Weyl semimetal bring the electron’s chirality degree of freedom into the electrical transport and give rise to exotic phenomena. A topological phase transition from a topological trivial phase to a topological non-trivial phase offers a route to control electronic devices through its topological properties. Here, we report the Weyl semimetal phase in hydrothermally grown two-dimensional Tellurium (2D Te) induced by high hydrostatic pressure (up to 2.47 GPa). The unique chiral crystal structure gives rise to chiral fermions with different topological chiral charges ($${{C}}=-{{1}},+{{1}},{{and}}-{{2}}$$ $C=-1,+1,and-2$). The highly tunable chemical potential in 2D Te provides comprehensive information for understanding the pressure-dependent electron band structure. The pressure-induced insulator-to-metal transition, two-carrier transport, and the non-trivial π Berry phase shift in quantum oscillations are observed in the 2D Te Weyl semimetal phase. Our work demonstrates the pressure-induced bandgap closing in the inversion asymmetric narrow bandgap semiconductor 2D Te. 

Abstract A key challenge in aerosol pollution studies and climate change assessment is to understand how atmospheric aerosol particles are initially formed^1,2. Although new particle formation (NPF) mechanisms have been described at specific sites^3–6, in most regions, such mechanisms remain uncertain to a large extent because of the limited ability of atmospheric models to simulate critical NPF processes^1,7. Here we synthesize molecular-level experiments to develop comprehensive representations of 11 NPF mechanisms and the complex chemical transformation of precursor gases in a fully coupled global climate model. Combined simulations and observations show that the dominant NPF mechanisms are distinct worldwide and vary with region and altitude. Previously neglected or underrepresented mechanisms involving organics, amines, iodine oxoacids and HNO₃probably dominate NPF in most regions with high concentrations of aerosols or large aerosol radiative forcing; such regions include oceanic and human-polluted continental boundary layers, as well as the upper troposphere over rainforests and Asian monsoon regions. These underrepresented mechanisms also play notable roles in other areas, such as the upper troposphere of the Pacific and Atlantic oceans. Accordingly, NPF accounts for different fractions (10–80%) of the nuclei on which cloud forms at 0.5% supersaturation over various regions in the lower troposphere. The comprehensive simulation of global NPF mechanisms can help improve estimation and source attribution of the climate effects of aerosols.