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We prove multi-point correlation bounds in$$\mathbb {Z}^d$$$Z^d$for arbitrary$$d\ge 1$$$d\ge 1$with symmetrized distances, answering open questions proposed by Sims–Warzel (Commun Math Phys 347(3):903–931, 2016) and Aza–Bru–Siqueira Pedra (Commun Math Phys 360(2):715–726, 2018). As applications, we prove multi-point correlation bounds for the Ising model on$$\mathbb {Z}^d$$$Z^d$, and multi-point dynamical localization in expectation for uniformly localized disordered systems, which provides the first examples of this conjectured phenomenon by Bravyi–König (Commun Math Phys 316(3):641–692, 2012) .

 

Clouds, crucial for understanding climate, begin with droplet formation from aerosols, but observations of this fleeting activation step are lacking in the atmosphere. Here we use a time-gated time-correlated single-photon counting lidar to observe cloud base structures at decimeter scales. Results show that the air–cloud interface is not a perfect boundary but rather a transition zone where the transformation of aerosol particles into cloud droplets occurs. The observed distributions of first-arriving photons within the transition zone reflect vertical development of a cloud, including droplet activation and condensational growth. Further, the highly resolved vertical profile of backscattered photons above the cloud base enables remote estimation of droplet concentration, an elusive but critical property to understanding aerosol–cloud interactions. Our results show the feasibility of remotely monitoring cloud properties at submeter scales, thus providing much-needed insights into the impacts of atmospheric pollution on clouds and aerosol-cloud interactions that influence climate.

 

Abstract Understanding the interactions between plants and microorganisms can inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on replicated plots of field-grown switchgrass and miscanthus, and to quantify their activities over two growing seasons for switchgrass. We use metagenome and metatranscriptome sequencing and curate 40 medium- and high-quality metagenome-assembled-genomes (MAGs). We find that classes represented by these MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) are active in the late season, and upregulate transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase. Stress-associated pathways are expressed for most MAGs, suggesting engagement with the host environment. We also detect seasonally activated biosynthetic pathways for terpenes and various non-ribosomal peptide pathways that are poorly annotated. Our findings support that leaf-associated bacterial populations are seasonally dynamic and responsive to host cues. 

The biofabrication of three-dimensional (3D) tissues that recapitulate organ-specific architecture and function would benefit from temporal and spatial control of cell-cell interactions. Bioprinting, while potentially capable of achieving such control, is poorly suited to organoids with conserved cytoarchitectures that are susceptible to plastic deformation. Here, we develop a platform, termed Spatially Patterned Organoid Transfer (SPOT), consisting of an iron-oxide nanoparticle laden hydrogel and magnetized 3D printer to enable the controlled lifting, transport, and deposition of organoids. We identify cellulose nanofibers as both an ideal biomaterial for encasing organoids with magnetic nanoparticles and a shear-thinning, self-healing support hydrogel for maintaining the spatial positioning of organoids to facilitate the generation of assembloids. We leverage SPOT to create precisely arranged assembloids composed of human pluripotent stem cell-derived neural organoids and patient-derived glioma organoids. In doing so, we demonstrate the potential for the SPOT platform to construct assembloids which recapitulate key developmental processes and disease etiologies.

 

Collisional growth of cloud droplets is an essential yet uncertain process for drizzle and precipitation formation. To improve the quantitative understanding of this key component of cloud‐aerosol‐turbulence interactions, observational studies of collision‐coalescence in a controlled laboratory environment are needed. In an existing convection‐cloud chamber (the Pi Chamber), collisional growth is limited by low liquid water content and short droplet residence times. In this work, we use numerical simulations to explore various configurations of a convection‐cloud chamber that may intensify collision‐coalescence. We employ a large‐eddy simulation (LES) model with a size‐resolved (bin) cloud microphysics scheme to explore how cloud properties and the intensity of collision‐coalescence are affected by the chamber size and aspect ratio, surface roughness, side‐wall wetness, side‐wall temperature arrangement, and aerosol injection rate. Simulations without condensation and evaporation within the domain are first performed to explore the turbulence dynamics and wall fluxes. The LES wall fluxes are used to modify the Scalar Flux‐budget Model, which is then applied to demonstrate the need for non‐uniform side‐wall temperature (two side walls as warm as the bottom and the two others as cold as the top) to maintain high supersaturation in a tall chamber. The results of LES with full cloud microphysics reveal that collision‐coalescence is greatly enhanced by employing a taller chamber with saturated side walls, non‐uniform side‐wall temperature, and rough surfaces. For the conditions explored, although lowering the aerosol injection rate broadens the droplet size distribution, favoring collision‐coalescence, the reduced droplet number concentration decreases the frequency of collisions.

 

Microbial communities are known as the primary decomposers of all the carbon accumulated in the soil. However, how important soil structure and its conventional or organic management, moisture content, and how different plant species impact this process are less understood. To answer these questions, we generated a soil microcosm with decomposing corn and soy leaves, as well as soil adjacent to the leaves, and compared it to control samples. We then used high-throughput amplicon sequencing of the ITS and 16S rDNA regions to characterize these microbiomes. Leaf microbiomes were the least diverse and the most even in terms of OTU richness and abundance compared to near soil and far soil, especially in their bacterial component. Microbial composition was significantly and primarily affected by niche (leaves vs. soil) but also by soil management type and plant species in the fungal microbiome, while moisture content and pore sizes were more important drivers for the bacterial communities. The pore size effect was significantly dependent on moisture content, but only in the organic management type. Overall, our results refine our understanding of the decomposition of carbon residues in the soil and the factors that influence it, which are key for environmental sustainability and for evaluating changes in ecosystem functions.