Microorganism-regulated mechanisms of temperature effects on the performance of anaerobic digestion

Additional file 2. Additional tables

Localization and mobility edges in non-Hermitian disorder-free lattices

The non-Hermitian skin effect (NHSE) is a significant phenomenon observed in non-Hermitian systems under open boundary conditions, where the extensive bulk eigenstates tend to accumulate at the lattice edges. In this article, we investigate how an electric field affects the localization properties in a non-Hermitian mosaic Stark lattice, exploring the interplay between the Stark localization, mobility edge (ME), and the NHSE induced by nonreciprocity. We analytically obtain the Lyapunov exponent and the phase transition points as well as numerically calculate the density distributions and the spectral winding number. We reveal that in the nonreciprocal Stark lattice with the mosaic periodic parameter $\kappa=1$, there exists a critical electric field strength that describes the transition of the existence-nonexistence of NHSE and is inversely proportional to the lattice size. This transition is consistent with the real-complex transition and topological transition characterized by spectral winding number under periodic boundary conditions. In the strong fields, the Wannier-Stark ladder is recovered, and the Stark localization is sufficient to suppress the NHSE. When the mosaic period $\kappa=2$, we show that the system manifests an exact non-Hermitian ME and the skin states are still existing in the strong fields, in contrast to the gigantic field can restrain the NHSE in the $\kappa=1$ case. Moreover, we further study the expansion dynamics of an initially localized state and dynamically probe the existence of the NHSE and the non-Hermitian ME. These results could help us to control the NHSE and the non-Hermitian ME by using electric fields in the disorder-free systems

Multiple localization transitions and novel quantum phases induced by staggered on-site potential

We propose an one-dimensional generalized Aubry-Andr{\'e}-Harper (AAH) model with off-diagonal hopping and staggered on-site potential. We find that the localization transitions could be multiple reentrant with the increasing of staggered on-site potential. The multiple localization transitions are verified by the quantum static and dynamic measurements such as the inversed or normalized participation ratios, fractal dimension and survival probability. Based on the finite-size scaling analysis, we also obtain an interesting intermediate phase where the extended, localized and critical states are coexistent in certain regime of model parameters. These results are quite different from those in the generalized AAH model with off-diagonal hopping, and can help us to find novel quantum phases, new localization phenomena in the disordered systems

Different Selectivity in Fungal Communities Between Manure and Mineral Fertilizers: A Study in an Alkaline Soil After 30 Years Fertilization

Fertilizer application has contributed substantially to increasing crop yield. Despite the important role of soil fungi in agricultural production, we still have limited understanding of the complex responses of fungal taxonomic and functional groups to organic and mineral fertilization in long term. Here we report the responses of the fungal communities in an alkaline soil to 30-year application of mineral fertilizer (NP), organic manure (M) and combined fertilizer (NPM) by the Illumina HiSeq sequencing and quantitative real-time PCR to target fungal internal transcribed spacer (ITS) genes. The results show: (1) compared to the unfertilized soil, fertilizer application increased fungal diversity and ITS gene copy numbers, and shifted fungal community structure. Such changes were more pronounced in the M and NPM soils than in the NP soil (except for fungal diversity), which can be largely attributed to the manure induced greater increases in soil total organic C, total N and available P. (2) Compared to the unfertilized soil, the NP and NPM soils reduced the proportion of saprotrophs by 40%, the predominant taxa of which may potentially affect cellulose decomposition. (3) Indicator species analysis suggested that the indicator operational taxonomic units (OTUs) in the M soil occupied 25.6% of its total community, but that only accounted for 0.9% in the NP soil. Our findings suggest that fertilization-induced changes of total fungal community were more responsive to organic manure than mineral fertilizer. The reduced proportion of cellulose decomposition-related saprotrophs in mineral fertilizer treatments may potentially contribute to increasing their soil C stocks

Elevational distribution and seasonal dynamics of alpine soil prokaryotic communities

The alpine grassland ecosystem is a biodiversity hotspot of plants on the Qinghai-Tibetan Plateau, where rapid climate change is altering the patterns of plant biodiversity along elevational and seasonal gradients of environments. However, how belowground microbial biodiversity changes along elevational gradient during the growing season is not well understood yet. Here, we investigated the elevational distribution of soil prokaryotic communities by using 16S rRNA amplicon sequencing along an elevational gradient between 3,200 and 4,200 m, and a seasonal gradient between June and September in the Qinghai-Tibetan alpine grasslands. First, we found soil prokaryotic diversity and community composition significantly shifted along the elevational gradient, mainly driven by soil temperature and moisture. Species richness did not show consistent elevational trends, while those of evenness declined with elevation. Copiotrophs and symbiotic diazotrophs declined with elevation, while oligotrophs and AOB increased, affected by temperature. Anaerobic or facultatively anaerobic bacteria and AOA were hump-shaped, mainly influenced by moisture. Second, seasonal patterns of community composition were mainly driven by aboveground biomass, precipitation, and soil temperature. The seasonal dynamics of community composition indicated that soil prokaryotic community, particularly Actinobacteria, was sensitive to short-term climate change, such as the monthly precipitation variation. At last, dispersal limitation consistently dominated the assembly process of soil prokaryotic communities along both elevational and seasonal gradients, especially for those of rare species, while the deterministic process of abundant species was relatively higher at drier sites and in drier July. The balance between deterministic and stochastic processes in abundant subcommunities might be strongly influenced by water conditions (precipitation/moisture). Our findings suggest that both elevation and season can alter the patterns of soil prokaryotic biodiversity in alpine grassland ecosystem of Qinghai-Tibetan Plateau, which is a biodiversity hotspot and is experiencing rapid climate change. This work provides new insights into the response of soil prokaryotic communities to changes in elevation and season, and helps us understand the temporal and spatial variations in such climate change-sensitive regions

MultiFun-DAG: Multivariate Functional Directed Acyclic Graph

Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex systems under a facile and oversimplified form. This paper considers that nodes can be multivariate functional data and thus proposes a multivariate functional DAG (MultiFun-DAG). It constructs a hidden bilinear multivariate function-to-function regression to describe the causal relationships between different nodes. Then an Expectation-Maximum algorithm is used to learn the graph structure as a score-based algorithm with acyclic constraints. Theoretical properties are diligently derived. Prudent numerical studies and a case study from urban traffic congestion analysis are conducted to show MultiFun-DAG's effectiveness

Enhanced Molecular Stacking Enabled by Photo‐Induced Cosslinking of Hole Transport Materials for High‐Performance QLED

Solution-processed quantum dot light-emitting diodes (QLEDs) are attractive candidates for next-generation displays. A critical component of high-performance QLEDs is a robust hole-transporting layer (HTL) with well-aligned energy levels. However, conventional polymer HTLs often suffer from disordered molecular stacking and severe tail states, leading to insufficient hole transport mobility, imbalanced carrier transport efficiency, and consequently, degraded device performance. To address these challenges, this study proposes a crosslinking-induced structural reforming strategy to optimize the polymer HTLs. As proof of this concept, poly(9,9-dioctylfluorenyl-2,7-diyl)-co-(4,4′-(N-(4-sec- butylphenyl)) diphenylamine) (TFB), the commonly used HTL material, is modified by adding a photo-crosslinking agent. The crosslinked TFB layers exhibit enhanced molecular ordering and narrowed tail states, suggesting reduced energetic disorder. The red QLED devices using crosslinked TFB as the HTL have shown significant improvement in performance, achieving peak external quantum efficiency (EQE) of 24.62% and current efficiency (CE) of 24.3 cd A−1. Furthermore, the operational stability is also improved, with a nearly three-fold enhancement compared to the control sample. Additionally, the photo-crosslinking process enables the precise patterning of TFB films, supporting the fabrication of pixelated HTLs. These results highlight the potential of crosslinked HTLs for enhancing performance and promoting commercialization in next-generation QLED displays

Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age- and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features