There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition. We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled “help”/“harm”), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals phase transitions in both connectivity and resilience. Our findings extend classical random threshold and Erdős-Rényi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil.
To holistically understand the biology of animals, we must unravel the complexities and specificities of host-microbe interactions across animal taxa. Birds represent enigmatic and scientifically compelling hosts in which to understand these interactions. Here, we present a brief summary of a series of conversations among avian microbiome researchers regarding methodological challenges facing the avian microbiome field, where most research to date has focused on bacterial communities of the gut. Collectively, we acknowledged a commonly shared but underreported issue facing the avian microbiome field: that of difficulty in obtaining high-quality and high-yield microbial DNA from avian fecal samples. We discuss some of the potential reasons underlying low DNA yields, such as inhibitory compounds and rapid DNA degradation, and provide recommendations for how researchers in the avian microbiome field might cope with these methodological challenges. Collective and dedicated efforts to address these challenges will be required for a robust understanding of host-microbe interactions in avian systems.
During conversations in the presence of other competing talkers, multiple speech streams compete for listeners' attentional focus. Listeners must segregate speech streams, and selectively attend to the target talker while filtering out irrelevant speech. Research on value-driven attention suggests that perceptual and attentional processes are biased by prior rewarding experi-ences with stimuli-that is, high-valued stimuli win attentional competition. However, it remains unknown whether such effects generalize to speech perception in multitalker environments. Here, the present study investigated whether listeners can better understand speech spoken by talkers associated with higher versus lower values in challenging listening conditions. In three experiments, we used reward-based training paradigms to either explicitly or implicitly induce listeners to associate talkers' voices with varying magnitudes of rewards. Subsequently, listeners performed a speech-on-speech intelligibility task in which the exposed voices were either the target or distractor. We found that explicit talker-reward learning did not influence speech intelligibility. In contrast, we observed that implicitly acquired talker-reward associations improved the intelligibility of high-reward talkers, but only in the most adverse listening condition. However, this effect did not replicate when we further imposed greater attentional demands by presenting target and masker talkers' speech without spatial sepa-ration. Instead, listeners relied primarily on acoustic cues other than reward-associated talkers' voices. Furthermore, the reward values associated with the distractor talker had no effect on interference. These findings suggest that value associ-ated with talkers' voices do not reliably bias listeners' attention to overcome the complex, acoustic constraints imposed in speech-on-speech perception.
Cancer is defined as mutations or abnormal changes in genes that regulate cell growth and maintain their health. When these altered cells continue to divide uncontrollably, they can generate similar cells, ultimately forming a tumor. The interactions among genes can influence the rate of cancer development. Gene expression technology allows simultaneous measurement of thousands of genes in a single experiment. However, analyzing the data is challenging because of its complexity, heterogeneity, and intricate interconnections among genes. Network science offers a promising framework for studying genetic data, by representing gene-gene relationships explicitly, enabling structured information sharing, noise averaging, and improved generalization. Using breast cancer gene expression data, this study systematically constructs gene networks and investigates how different network topologies and properties influence the predictive performance of a Graph Convolutional Neural Network (GCNN) model. Network science analysis inspired the development of two network-aware feature selection approaches, leveraging more sophisticated mathematical techniques based on network science to reduce model complexity. The findings indicate that a correlation threshold of 0.5 acts as an optimal point, consistently yielding the best results for GCNN models. Model performance is influenced by the network topology. While stronger connectivity can improve performance, there may be a threshold beyond which excessive connections actually impede the flow of information.
A new optimized LiFSI-LiPF6 dual-salt controlled-solvation electrolyte (E-DS) is demonstrated to enable practical graphite||LiNi0.8Mn0.1Co0.1O2 cells (approximate to 4.1 mAh cm(-2)) to achieve exceptional performance and safety under extreme conditions. By optimizing anion coordination with the smaller, more dissociating FSI- anion, the E-DS forms ultrathin, dense, and inorganic-rich electrode/electrolyte interphases that dramatically suppress solvent decomposition, transition-metal dissolution, and surface reconstruction compared to the conventional LiPF6/carbonate electrolyte. Consequently, E-DS cells deliver >78% capacity retention after 300 cycles at 60 degrees C, retain fast discharging capacity at 30 degrees C, and operate effectively at -20 degrees C. Most strikingly, fully charged full cells with E-DS, even under overcharging to 4.8 V and elevated temperatures, show a lower heat evolution in stable formulations-transforming a traditionally unstable high-voltage/high-temperature configuration into an intrinsically safe state. This work establishes a new benchmark for carbonate-containing electrolytes, simultaneously achieving high energy density, fast-discharging capability, wide-temperature operation (-20 to 60 degrees C), and outstanding thermal safety in nickel-rich lithium-ion batteries.
