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Lipid droplets (LDs) are multi-functional organelles consisting of a neutral lipid core surrounded by a phospholipid monolayer, and exist in organisms ranging from bacteria to humans. Here we study the functions of LDs in the oleaginous bacterium Rhodococcus jostii . We show that these LDs bind to genomic DNA through the major LD protein, MLDS, which increases survival rate of the bacterial cells under nutritional and genotoxic stress. MLDS expression is regulated by a transcriptional regulator, MLDSR, that binds to the operator and promoter of the operon encoding both proteins. LDs sequester MLDSR, controlling its availability for transcriptional regulation. Our findings support the idea that bacterial LDs can regulate nucleic acid function and facilitate bacterial survival under stress. The MLDS protein is a major component of lipid droplets (LDs) in oleaginous bacteria. Here, Zhang et al . show that LDs bind to genomic DNA via MLDS, which enhances bacterial survival under certain stress conditions.

Although the elementary unit of biology is the cell, high-throughput methods for the microscale manipulation of cells and reagents are limited. The existing options either are slow, lack single-cell specificity, or use fluid volumes out of scale with those of cells. Here we present printed droplet microfluidics, a technology to dispense picoliter droplets and cells with deterministic control. The core technology is a fluorescence-activated droplet sorter coupled to a specialized substrate that together act as a picoliter droplet and single-cell printer, enabling high-throughput generation of intricate arrays of droplets, cells, and microparticles. Printed droplet microfluidics provides a programmable and robust technology to construct arrays of defined cell and reagent combinations and to integrate multiple measurement modalities together in a single assay.

Eukaryotic cells compartmentalize neutral lipids into organelles called lipid droplets (LDs), and while much is known about the role of LDs in storing triacylglycerols (TAGs) in seeds, their biogenesis and function in non-seed tissues is poorly understood. Recently, we identified a class of plant-specific, LD-associated proteins (LDAPs) that are abundant components of LDs in non-seed cell types. Here, we characterized the three LDAPs in Arabidopsis thaliana to gain insight to their targeting, assembly and influence on LD function and dynamics. While all three LDAPs targeted with high specificity to the LD surface, truncation analysis of LDAP3 revealed that essentially the entire protein was required for LD localization. The association of LDAP3 with LDs was detergent-sensitive, but the protein bound with similar affinity to synthetic liposomes of various phospholipid compositions, suggesting that other factors contributed to targeting specificity. Investigation of LD dynamics in leaves revealed that LD abundance was modulated during the diurnal cycle, and characterization of LDAP mis-expression mutants indicated that all three LDAPs were important for this process. LD abundance was significantly increased during abiotic stress, and characterization of mutant lines revealed that LDAP3 was required for induction of LDs during cold temperature stress, while LDAP1 was required for heat stress. Furthermore, LDAP1 was required for proper neutral lipid compartmentalization and TAG mobilization during post-germinative growth. Taken together, these studies reveal that LDAPs are required for the maintenance and regulation of LDs in plant cells and perform non-redundant functions in various physiological contexts, including stress response and post-germinative growth.

We investigate the effect of turbulence on the collisional growth of micrometer-sized droplets through high-resolution numerical simulations with well-resolved Kolmogorov scales, assuming a collision and coalescence efficiency of unity. The droplet dynamics and collisions are approximated using a superparticle approach. In the absence of gravity, we show that the time evolution of the shape of the droplet-size distribution due to turbulence-induced collisions depends strongly on the turbulent energy-dissipation rate [Formula: see text], but only weakly on the Reynolds number. This can be explained through the [Formula: see text] dependence of the mean collision rate described by the Saffman–Turner collision model. Consistent with the Saffman–Turner collision model and its extensions, the collision rate increases as [Formula: see text] even when coalescence is invoked. The size distribution exhibits power-law behavior with a slope of −3.7 from a maximum at approximately 10 up to about 40 μm. When gravity is invoked, turbulence is found to dominate the time evolution of an initially monodisperse droplet distribution at early times. At later times, however, gravity takes over and dominates the collisional growth. We find that the formation of large droplets is very sensitive to the turbulent energy dissipation rate. This is because turbulence enhances the collisional growth between similar-sized droplets at the early stage of raindrop formation. The mean collision rate grows exponentially, which is consistent with the theoretical prediction of the continuous collisional growth even when turbulence-generated collisions are invoked. This consistency only reflects the mean effect of turbulence on collisional growth.

This study aimed to improve and investigate the oscillation dynamics and levitation stability of acoustically levitated water droplets. Contactless sample manipulation technology in mid-air has attracted significant attention in the fields of biochemistry and pharmaceutical science. Although one promising method is acoustic levitation, most studies have focused on a single sample. Therefore, it is important to determine the stability of multiple samples during acoustic levitation. Here, we aim to understand the effect of multiple-sample levitation on levitation stability in acoustic fields. We visualized the oscillatory motion of multiple levitated droplets using a high-speed video camera. To characterize the dynamics of multiple levitating droplets, the oscillation frequency and restoring force coefficients of the levitated samples, which were obtained from the experimental data, were analyzed to quantify the droplet–droplet interaction. The oscillation model of the spring-mass system was compared with the experimental results, and we found that the number of levitating droplets and their position played an important role in the levitation stability of the droplets. Our insights could help us understand the oscillatory behavior of levitated droplets to achieve more stable levitation.

Changes in aerosol amount and size distribution significantly impact cloud droplet size distribution, as aerosols act as cloud condensation nuclei (CCNs) and influence the relative dispersion (ε) of cloud droplet spectra. Relative dispersion plays a key role in parameterizing cloud processes in general circulation models (GCMs) and microphysical schemes, affecting precipitation estimates and climate predictions. However, the effects of varying aerosol modes on cloud microphysics remain debated, depending on thermodynamic conditions and cloud type. This study simulates a warm stratiform cloud in Jiangxi, China, using the Weather Research and Forecasting (WRF) Spectra–Bin Microphysics scheme (SBM-FAST) from 18:00 on 24 December 2014 to 06:00 on 25 December 2014 (UTC). Satellite and aircraft observations were used to validate the simulation, showing good agreement in cloud structure. Sensitivity experiments were conducted by increasing nucleation, accumulation, and coarse-mode aerosols 5-fold and by reducing the total aerosol concentration to 1/5 of the control. Results show that higher aerosol concentrations enhance cloud formation and broaden droplet spectra, while lower concentrations suppress cloud development. Accumulation-mode aerosols increase small-droplet concentrations, while nucleation- and coarse-mode aerosols favor larger droplets. The correlation between ε and volume-weighted radius (Rv) shifts from positive to negative as Rv increases. This transition is driven by cloud droplet collision–coalescence, condensation, and activation. Increased accumulation-mode aerosol concentrations shift the ε–Rv correlation from negative to positive in the Rv range of 4.5–8 µm, while reduced aerosol concentrations strengthen the negative correlation. Regardless of different coalescence intensities, ε converges with the increase in number concentration of cloud droplets (Nc).

Relative dispersion (ε), as a parameter characterizing droplet spectral shape, exerts a considerable impact on cloud radiation and precipitation processes, and its accurate parameterization is urgently needed in models. Current ε parameterizations, which are based on droplet number concentration or simply set as constants, are inadequate to satisfy the demand. This study shows, utilizing in‐situ cloud and fog observations from five underlying surface regions (urban, suburban, mountainous, coastal and rainforest) of China, that ε uniformly and stably manifests as initially increasing then decreasing as volume‐mean diameter increases across these regions. Based on this relationship, a ε parameterization is established, which exhibits improved predictive capabilities in evaluating both cloud albedo effect and cloud lifetime effect. The parameterization is expected to enhance cloud simulation accuracy and minimize discrepancy between observed and simulated cloud radiation and precipitation, particularly for weather and climate models that commonly use the double‐moment cloud microphysical schemes. Plain Language Summary Clouds play a crucial role in the Earth's weather and climate. One key factor in understanding cloud behavior is the width of cloud droplet size distribution, quantified by relative dispersion. Accurately representing relative dispersion in weather and climate models is essential, yet current methods are often overly simplistic. Many models either rely on fixed values or use empirical monotonic equations based solely on droplet number concentration. However, the relationship between relative dispersion and droplet number concentration varies significantly across regions and can even be contradictory. In this study, we analyzed cloud and fog observations from five different regions, encompassing urban, suburban, mountainous, coastal, and rainforest environments. Our analysis revealed a consistent pattern: relative dispersion first increases and then decreases as the volume‐mean droplet diameter grows. Based on this insight, we developed a new method to predict relative dispersion. This approach has the potential to improve the accuracy of estimating cloud albedo and lifetime effects, enhancing the representation of aerosol‐cloud interactions in weather and climate models. Key Points Correlation between droplet spectral dispersion and volume‐mean diameter remains consistent across different regions Compared to previous dispersion parameterizations, the parameterization with volume‐mean diameter provides better predictions for dispersion The dispersion parameterization with volume‐mean diameter could reduce the uncertainty in simulating aerosol indirect effects

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

Lipid droplets were long considered to be simple storage structures, but they have recently been shown to be dynamic organelles involved in diverse biological processes, including emerging roles in innate immunity. Various intracellular pathogens, including viruses, bacteria, and parasites, specifically target host lipid droplets during their life cycle. Viruses such as hepatitis C, dengue, and rotaviruses use lipid droplets as platforms for assembly. Bacteria, such as mycobacteria and Chlamydia, and parasites, such as trypanosomes, use host lipid droplets for nutritional purposes. The possible use of lipid droplets by intracellular pathogens, as part of an anti‐immunity strategy, is an intriguing question meriting further investigation in the near future.

With the variety of fibers and fabrics, the studies of the surface structure of the textile yarns, the weave fabric, and their surface wettability are still potential factors to improve and optimize the fog harvesting efficiency. In this work, inspired by the fog harvesting behavior of the desert beetle dorsal surface, a wavy–bumpy structure of post-weave yarn (obtained from woven fabric) was reported to improve large droplet growth (converge) efficiency. In which, this study used tetrabutyl titanate (Ti(OC4H9)4) to waterproof, increase hydrophobicity, and stabilize the surface of yarns and fabric (inspired by the feather structure and lotus leaf surface). Moreover, PDMS oil was used (lubricated) to increase hydrophobicity and droplet shedding on the yarns (inspired by the slippery surface of the pitcher plant) and at the same time, enhance the fog harvesting efficiency of the warp yarn woven fabric (Warp@fabric). In addition, a three-dimensional adjacent yarn structure was arranged by two non-parallel fabric layers. The yarns of the inner and outer layers were intersected at an angle decreasing to zero (mimicking the water transport behavior of Shorebird’s beaks). This method helped large droplets quickly form and shed down easily. More than expected, the changes in fabric texture and fiber surface yielded an excellent result. The OBLWB-Warp@fabric’s water harvesting rate was about 700% higher than that of the original plain weave fabric (Original@fabric). OBLWB-Warp@fabric’s water harvesting rate was about 160% higher than that of Original–Warp@fabric. This shows the great practical application potential of woven fabrics with a low cost and large scale, or you can make use of textile wastes to collect fog, suitable for the current circular economy model. This study hopes to further enrich the materials used for fog harvesting.