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Penicillin-binding proteins (PBPs) play critical roles in cell wall construction, cell shape maintenance, and bacterial replication. Bacteria maintain a diversity of PBPs, indicating that despite their apparent functional redundancy, there is differentiation across the PBP family. Apparently-redundant proteins can be important for enabling an organism to cope with environmental stressors. In this study, we evaluated the consequence of environmental pH on PBP enzymatic activity inBacillus subtilis. Our data show that a subset of PBPs inB. subtilischange activity levels during alkaline shock and that one PBP isoform is rapidly modified to generate a smaller protein (i.e., PBP1a to PBP1b). Our results indicate that a subset of the PBPs are favored for growth under alkaline conditions, while others are readily dispensable. Indeed, we found that this phenomenon could also be observed inStreptococcus pneumoniae, implying that it may be generalizable across additional bacterial species and further emphasizing the evolutionary benefit of maintaining many, seemingly-redundant periplasmic enzymes.

Microbes adapt to ever-changing environments and thrive over a vast range of conditions. While bacterial genomes are relatively small, significant portions encode for “redundant” functions. Apparent redundancy is especially pervasive in bacterial proteins that reside outside of the inner membrane. While conditions within the cytoplasm are carefully controlled, those of the periplasmic space are largely determined by the cell’s exterior environment. As a result, proteins within this environmentally exposed region must be capable of functioning under a vast array of conditions, and/or there must be several similar proteins that have evolved to function under a variety of conditions. This study examines the activity of a class of enzymes that is essential in cell wall construction to determine if individual proteins might be adapted for activity under particular growth conditions. Our results indicate that a subset of these proteins are preferred for growth under alkaline conditions, while others are readily dispensable.

 

Morphological properties of tissues and organs rely on cell growth. The growth of plant cells is determined by properties of a tough outer cell wall that deforms anisotropically in response to high turgor pressure. Cortical microtubules bias the mechanical anisotropy of a cell wall by affecting the trajectories of cellulose synthases in the wall that polymerize cellulose microfibrils. The microtubule cytoskeleton is often oriented in one direction at cellular length-scales to regulate growth direction, but the means by which cellular-scale microtubule patterns emerge has not been well understood. Correlations between the microtubule orientation and tensile forces in the cell wall have often been observed. However, the plausibility of stress as a determining factor for microtubule patterning has not been directly evaluated to date.

Here, we simulated how different attributes of tensile forces in the cell wall can orient and pattern the microtubule array in the cortex. We implemented a discrete model with transient microtubule behaviors influenced by local mechanical stress in order to probe the mechanisms of stress-dependent patterning. Specifically, we varied the sensitivity of four types of dynamic behaviors observed on the plus end of microtubules – growth, shrinkage, catastrophe, and rescue – to local stress. Then, we evaluated the extent and rate of microtubule alignments in a two-dimensional computational domain that reflects the structural organization of the cortical array in plant cells.

Our modeling approaches reproduced microtubule patterns observed in simple cell types and demonstrated that a spatial variation in the magnitude and anisotropy of stress can mediate mechanical feedback between the wall and of the cortical microtubule array.

 

Hybridization in plants is often accompanied by nuclear genome doubling (allopolyploidy), which has been hypothesized to perturb interactions between nuclear and organellar (mitochondrial and plastid) genomes by creating imbalances in the relative copy number of these genomes and producing genetic incompatibilities between maternally derived organellar genomes and the half of the allopolyploid nuclear genome from the paternal progenitor. Several evolutionary responses have been predicted to ameliorate these effects, including selection for changes in protein sequences that restore cytonuclear interactions; biased gene retention/expression/conversion favoring maternal nuclear gene copies; and fine-tuning of relative cytonuclear genome copy numbers and expression levels. Numerous recent studies, however, have found that evolutionary responses are inconsistent and rarely scale to genome-wide generalities. The apparent robustness of plant cytonuclear interactions to allopolyploidy may reflect features that are general to allopolyploids such as the lack of F2 hybrid breakdown under disomic inheritance, and others that are more plant-specific, including slow sequence divergence in organellar genomes and preexisting regulatory responses to changes in cell size and endopolyploidy during development. Thus, cytonuclear interactions may only rarely act as the main barrier to establishment of allopolyploid lineages, perhaps helping to explain why allopolyploidy is so pervasive in plant evolution.

 

This work investigates traffic control via controlled connected and automated vehicles (CAVs) using novel controllers derived from the linear-quadratic regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks impacting the surrounding traffic with their speeds as control inputs. An iterative controller algorithm based on the LQR theory is proposed along with a variant that allows for penalizing abrupt changes in platoon speeds. The controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an extended cell transmission model (CTM) which considers the capacity drop phenomenon for a realistic representation of traffic in congestion. The impact of various parameters of the proposed controller on the control performance is analyzed. The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI) and model predictive control (MPC) controllers from the literature. A case study using the TransModeler traffic microsimulation software is conducted to test the usability of the proposed controller as well as existing controllers in a realistic setting and derive qualitative insights. It is observed that the proposed controller works well in both settings to mitigate the impact of the jam caused by a fixed bottleneck. The computation time required by the controller is also small making it suitable for real-time control.

 

Abstract This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on and the method is available as a service through the BisQue portal. 

Improving the resolution of seismic anelastic models is critical for a better understanding of the Earth’s subsurface structure and dynamics. Seismic attenuation plays a crucial role in estimating water content, partial melting and temperature variations in the Earth’s crust and mantle. However, compared to seismic wave-speed models, seismic attenuation tomography models tend to be less resolved. This is due to the complexity of amplitude measurements and the challenge of isolating the effect of attenuation in the data from other parameters. Physical dispersion caused by attenuation also affects seismic wave speeds, and neglecting scattering/defocusing effects in classical anelastic models can lead to biased results. To overcome these challenges, it is essential to account for the full 3-D complexity of seismic wave propagation. Although various synthetic tests have been conducted to validate anelastic full-waveform inversion (FWI), there is still a lack of understanding regarding the trade-off between elastic and anelastic parameters, as well as the variable influence of different parameter classes on the data. In this context, we present a synthetic study to explore different strategies for global anelastic inversions.

To assess the resolution and sensitivity for different misfit functions, we first perform mono-parameter inversions by inverting only for attenuation. Then, to study trade-offs between parameters and resolution, we test two different inversion strategies (simultaneous and sequential) to jointly constrain the elastic and anelastic parameters. We found that a sequential inversion strategy performs better for imaging attenuation than a simultaneous inversion. We also demonstrate the dominance of seismic wave speeds over attenuation, underscoring the importance of determining a good approximation of the Hessian matrix and suitable damping factors for each parameter class.

 

Microplastics are globally ubiquitous in marine environments, and their concentration is expected to continue rising at significant rates as a result of human activity. They present a major ecological problem with well-documented environmental harm. Sea spray from bubble bursting can transport salt and biological material from the ocean into the atmosphere, and there is a need to quantify the amount of microplastic that can be emitted from the ocean by this mechanism. We present a mechanistic study of bursting bubbles transporting microplastics. We demonstrate and quantify that jet drops are efficient at emitting microplastics up to 280μm in diameter and are thus expected to dominate the emitted mass of microplastic. The results are integrated to provide a global microplastic emission model which depends on bubble scavenging and bursting physics; local wind and sea state; and oceanic microplastic concentration. We test multiple possible microplastic concentration maps to find annual emissions ranging from 0.02 to 7.4—with a best guess of 0.1—mega metric tons per year and demonstrate that while we significantly reduce the uncertainty associated with the bursting physics, the limited knowledge and measurements on the mass concentration and size distribution of microplastic at the ocean surface leaves large uncertainties on the amount of microplastic ejected.

 

The intercalated cells of the amygdala (ITCs) are a fundamental processing structure in the amygdala that remain relatively understudied. They are phylogenetically conserved from insectivores through primates, inhibitory, and project to several of the main processing and output stations of the amygdala and basal forebrain. Through these connections, the ITCs are best known for their role in conditioned fear, where they are required for fear extinction learning and recall. Prior work on ITC connectivity is limited, and thus holistic characterization of their afferent and efferent connectivity in a genetically defined manner is incomplete. The ITCs express theFoxP2transcription factor, affording genetic access to these neurons for viral input-output mapping. To fully characterize the anatomic connectivity of the ITCs, we used cre-dependent viral strategies in FoxP2-cre mice to reveal the projections of the main (mITC), caudal (cITC), and lateral (lITC) clusters along with their presynaptic sources of innervation. Broadly, the results confirm many known pathways, reveal previously unknown ones, and demonstrate important novel insights about each nucleus’s unique connectivity profile and relative distributions. We show that the ITCs receive information from a wide range of cortical, subcortical, basal, amygdalar, hippocampal, and thalamic structures, and project broadly to areas of the basal forebrain, hypothalamus, and entire extent of the amygdala. The results provide a comprehensive map of their connectivity and suggest that the ITCs could potentially influence a broad range of behaviors by integrating information from a wide array of sources throughout the brain.