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Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

The past years have seen the success of a novel and rigorous localized multiscale energetics formalism in a variety of ocean and engineering fluid applications. In a self-contained way, this study introduces it to the atmospheric dynamical diagnostics, with important theoretical updates and clarifications of some common misconceptions about multiscale energy. Multiscale equations are derived using a new analysis apparatus—namely, multiscale window transform—with respect to both the primitive equation and quasigeostrophic models. A reconstruction of the “atomic” energy fluxes on the multiple scale windows allows for a natural and unique separation of the in-scale transports and cross-scale transfers from the intertwined nonlinear processes. The resulting energy transfers bear a Lie bracket form, reminiscent of the Poisson bracket in Hamiltonian mechanics; hence, we would call them “canonical.” A canonical transfer process is a mere redistribution of energy among scale windows, without generating or destroying energy as a whole. By classification, a multiscale energetic cycle comprises available potential energy (APE) transport, kinetic energy (KE) transport, pressure work, buoyancy conversion, work done by external forcing and friction, and the cross-scale canonical transfers of APE and KE, which correspond respectively to the baroclinic and barotropic instabilities in geophysical fluid dynamics. A buoyancy conversion takes place in an individual window only, bridging the two types of energy, namely, KE and APE; it does not involve any processes among different scale windows and is hence basically not related to instabilities. This formalism is exemplified with a preliminary application to the study of the Madden–Julian oscillation.

Using a recently developed tool, multiscale window transform (MWT), and the MWT-based canonical energy transfer theory, this study investigates the seasonal eddy variability in the Kuroshio Extension. Distinct seasonal cycles of eddy kinetic energy (EKE) are observed in the upstream and downstream regions of the Kuroshio Extension. In the upstream Kuroshio Extension, the EKE peaks in summer and reaches its minimum in winter over an annual cycle. By diagnosing the spatiotemporal structures of the canonical barotropic and baroclinic energy transfers, we found that internal processes due to mixed instabilities (i.e., both barotropic and baroclinic instabilities) are responsible for the seasonal eddy variability in this region. In the downstream Kuroshio Extension, the EKE exhibits a different annual cycle, peaking in spring and gradually decaying from summer to winter. Significant inverse barotropic energy transfer is found in this region throughout the year, leaving baroclinic instability the primary energy source for the regional seasonal eddy variability. Besides the internal redistribution, it is also evident that the external forcing may influence the Kuroshio Extension EKE seasonality—the EKE is found to be more damped by winds during winter than summer.

The symmetry of Cooper pairs in iron-based superconductors is an issue under continued investigation. A scanning tunnelling study of Fe(Te,Se) reveals a robust zero-energy bound state, providing evidence for a non-trivial pairing symmetry. In superconductors, electrons are paired and condensed into the ground state. An impurity can break the electron pairs into quasiparticles with energy states inside the superconducting gap. The characteristics of such in-gap states reflect accordingly the properties of the superconducting ground state 1 . A zero-energy in-gap state is particularly noteworthy, because it can be the consequence of non-trivial pairing symmetry 1 or topology 2 , 3 . Here we use scanning tunnelling microscopy/spectroscopy to demonstrate that an isotropic zero-energy bound state with a decay length of ∼10 Å emerges at each interstitial iron impurity in superconducting Fe(Te,Se). More noticeably, this zero-energy bound state is robust against a magnetic field up to 8 T, as well as perturbations by neighbouring impurities. Such a spectroscopic feature has no natural explanation in terms of impurity states in superconductors with s -wave symmetry, but bears all the characteristics of the Majorana bound state proposed for topological superconductors 2 , 3 , indicating that the superconducting state and the scattering mechanism of the interstitial iron impurities in Fe(Te,Se) are highly unconventional.

We use a newly developed technique that is based on the information flow concept to investigate the causal structure between the global radiative forcing and the annual global mean surface temperature anomalies (GMTA) since 1850. Our study unambiguously shows one-way causality between the total Greenhouse Gases and GMTA. Specifically, it is confirmed that the former, especially CO 2 , are the main causal drivers of the recent warming. A significant but smaller information flow comes from aerosol direct and indirect forcing and on short time periods, volcanic forcings. In contrast the causality contribution from natural forcings (solar irradiance and volcanic forcing) to the long term trend is not significant. The spatial explicit analysis reveals that the anthropogenic forcing fingerprint is significantly regionally varying in both hemispheres. On paleoclimate time scales, however, the cause-effect direction is reversed: temperature changes cause subsequent CO 2 /CH 4 changes.

Abnormal expression of activating/inhibitory receptors leads to natural killer (NK) cells dysfunction in tumor. Here we show that programmed cell death protein 1 (PD-1), a well-known immune checkpoint of T cells, is highly expressed on peripheral and tumor-infiltrating NK cells from patients with digestive cancers including esophageal, liver, colorectal, gastric and biliary cancer. The increased PD-1 expression on NK cells indicates poorer survival in esophageal and liver cancers. Blocking PD-1/PD-L1 signaling markedly enhances cytokines production and degranulation and suppresses apoptosis of NK cells in vitro . PD-1/PD-L1 exerts inhibitory effect through repressing the activation of PI3K/AKT signaling in NK cells. More importantly, a PD-1 blocking antibody was found to significantly suppress the growth of xenografts in nude mice, and this inhibition of tumor growth was completely abrogated by NK depletion. These findings strongly suggested that PD-1 is an inhibitory regulator of NK cells in digestive cancers. PD-1 blockade might be an efficient strategy in NK cell-based tumor immunotherapy.

The water environment in Chinese rural areas has seriously deteriorated to the extent that the development of rural areas is now under threat. To deal with this issue, the Chinese government has been promoting wastewater treatment systems (WTS) in rural areas since 2005. However, some of these wastewater treatment plants have shut down after just a few years. Thus, even though the number of newly built plants in rural areas has been rapidly increasing, the failure of these plants can impede the development of an efficient wastewater treatment in Chinese rural areas. This paper investigates the challenges faced by the management of the constructed wastewater treatment plants in Chinese rural areas using the case study method. Two cases, of Zhejiang and Hainan provinces, were analysed separately; the operation was successful in the former province and a failure in the latter one. The study demonstrates that the WTS in rural areas are faced with diverse implementation challenges, ranging from unsustainable financial sources for operation and maintenance cost to inappropriate governance structure, potential risks due to the local leadership rotation system of the government, limited participation by farmers in the management and technical complexity.

Vegetation acclimation to changing climate, in particular elevated atmospheric concentrations of carbon dioxide (CO2), has been observed to include modifications to the biochemical and ecophysiological functioning of leaves and the structural components of the canopy. These responses have the potential to significantly modify plant carbon uptake and surface energy partitioning, and have been attributed with large-scale changes in surface hydrology over recent decades. While the aggregated effects of vegetation acclimation can be pronounced, they often result from subtle changes in canopy properties that require the resolution of physical, biochemical and ecophysiological processes through the canopy for accurate estimation. In this paper, the first of two, a multilayer canopy-soil-root system model developed to capture the emergent vegetation responses to environmental change is presented. The model incorporates both C3 and C4 photosynthetic pathways, and resolves the vertical radiation, thermal, and environmental regimes within the canopy. The tight coupling between leaf ecophysiological functioning and energy balance determines vegetation responses to climate states and perturbations, which are modulated by soil moisture states through the depth of the root system. The model is validated for three growing seasons each for soybean (C3) and maize (C4) using eddy-covariance fluxes of CO2, latent, and sensible heat collected at the Bondville (Illinois) Ameriflux tower site. The data set provides an opportunity to examine the role of important environmental drivers and model skill in capturing variability in canopy-atmosphere exchange. Vertical variation in radiative states and scalar fluxes over a mean diurnal cycle are examined to understand the role of canopy structure on the patterns of absorbed radiation and scalar flux magnitudes and the consequent differences in sunlit and shaded source/sink locations through the canopies. An analysis is made of the impact of soil moisture stress on carbon uptake and energy flux partitioning at the canopy-scale and resolved through the canopy, providing insight into the roles of canopy structure and metabolic pathway on the response of each crop to moisture deficits. Model calculations indicate increases in water use efficiency (WUE) with increasing moisture stress, with average maize WUE increases of 45% at the highest levels of plant stress examined here, relative to 20% increases for soybean.

The past decade has seen growing applications of the information flow-based causality analysis, particularly with the concise formula of its maximum likelihood estimator. At present, the algorithm for its estimation is based on differential dynamical systems, which, however, may raise an issue for coarsely sampled time series. Here, we show that, for linear systems, this is suitable at least qualitatively, but, for highly nonlinear systems, the bias increases significantly as the sampling frequency is reduced. This study provides a partial solution to this problem, showing how causality analysis can be made faithful with coarsely sampled series, provided that the statistics are sufficient. The key point here is that, instead of working with a Lie algebra, we turn to work with its corresponding Lie group. An explicit and concise formula is obtained, with only sample covariances involved. It is successfully applied to a system comprising a pair of coupled Rössler oscillators. Particularly remarkable is the success when the two oscillators are nearly synchronized. As more often than not observations may be scarce, this solution, albeit partial, is very timely.

•ASD exhibits altered hierarchical brain connectivity patterns constructed by stepwise causal connections.•DMN and VAN are major information outflow nodes in ASD, which are associated with social and communication disorders.•The connectivity of LN and FPN as information inflow nodes is reduced in ASD.•Our findings may provide new directions for brain network research from the perspective of excitatory and inhibitory brain regulations. Autism spectrum disorder (ASD) is associated with atypical brain connectivity, yet its hierarchical organization remains underexplored. In this study, we applied the Liang information flow method to analyze stepwise causal functional connectivity in ASD, offering a novel approach to understanding how different brain networks interact. Using resting-state fMRI data from ASD individuals and healthy controls, we observed significant alterations in both positive and negative causal connections across the ventral attention network, limbic network, frontal-parietal network, and default mode network. These disruptions were detected at multiple hierarchical levels, indicating changes in communication patterns across brain regions. By leveraging features of hierarchical causal connectivity, we achieved high classification accuracy between ASD and healthy individuals. Additionally, changes in network node degrees were found to correlate with ASD clinical symptoms, particularly social and communication behaviors. Our findings provide new insights into disrupted hierarchical brain connectivity in ASD and demonstrate the potential of this approach for distinguishing ASD from typical development.