Beyond abiotic conditions, do population dynamics mostly depend on a species\' direct predators, preys and conspecifics? Or can indirect feedback that ripples across the whole community be equally important? Determining where ecological communities sit on the spectrum between these two characterizations requires a metric able to capture the difference between them. Here we show that the spectral radius of a community\'s interaction matrix provides such a metric, thus a measure of ecological collectivity, which is accessible from imperfect knowledge of biotic interactions and related to observable signatures. This measure of collectivity integrates existing approaches to complexity, interaction structure and indirect interactions. Our work thus provides an original perspective on the question of to what degree communities are more than loose collections of species or simple interaction motifs and explains when pragmatic reductionist approaches ought to suffice or fail when applied to ecological communities.

PM2.5 is a complex pollutant that is a pervasive threat to human health. The health risks and toxicity mechanisms of PM2.5 components must be identified to alleviate the corresponding risks. In this study, a reductionism approach based on model PM2.5 particles was used to investigate the contributions of the most harmful components in PM2.5 to its toxicity. Human liver and kidney cells were used as models. The results showed that Cr(VI) was the most critical toxic component among other components (Pb, As, and benzo[a]pyrene) in human liver and kidney cells. PM2.5-Cr(VI) induced oxidative stress, which led to cytotoxicity by inducing cell cycle arrest in the S-phase in HepG2 and HEK293 cells. The presented findings can provide valuable insights into the toxicity levels of PM2.5 components, which can help clarify the potential health risks from PM2.5 exposure.

Based on a material view and reductionism, science has achieved great success. These cognitive paradigms treat the external as an objective existence and ignore internal consciousness. However, this cognitive paradigm, which we take for granted, has also led to some dilemmas related to consciousness in biology and physics. Together, these phenomena reveal the interaction and inseparable side of matter and consciousness (or body and mind) rather than the absolute opposition. However, a material view that describes matter and consciousness in opposition cannot explain the underlying principle, which causes a gap in interpretation. For example, consciousness is believed to be the key to influencing wave function collapse (reality), but there is a lack of a scientific model to study how this happens. In this study, we reveal that the theory of scientific cognition exhibits a paradigm shift in terms of perception. This tendency implies that reconciling the relationship between matter and consciousness requires an abstract theoretical model that is not based on physical forms. We propose that the holistic cognitive paradigm offers a potential solution to reconcile the dilemmas and can be scientifically proven. In contrast to the material view, the holistic cognitive paradigm is based on the objective contradictory nature of perception rather than the external physical characteristics. This cognitive paradigm relies on perception and experience (not observation) and summarizes all existence into two abstract contradictory perceptual states (Yin-Yang). Matter and consciousness can be seen as two different states of perception, unified in perception rather than in opposition. This abstract perspective offers a distinction from the material view, which is also the key to falsification, and the occurrence of an event is inseparable from the irrational state of the observer\'s conscious perception. Alternatively, from the material view, the event is random and has nothing to do with perception. We hope that this study can provide some new enlightenment for the scientific coordination of the opposing relationship between matter and consciousness.

Gaining insight into the response of surface ozone (O3) formation to its precursors plays an important role in the policy-making of O3 pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O3 and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O3 and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O3 prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O3 and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O3 concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O3 formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O3 formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O3 and its precursors could be used to test and improve the simulation performance of modern models.