Unveiling bacterial communication with a MATLAB GUI implementing the diffusion-based quorum sensing model.

Bacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role.

Decoding the physiological response of plants to stress using deep learning for forecasting crop loss due to abiotic, biotic, and climatic variables.

This paper presents a simple method for detecting both biotic and abiotic stress in plants. Stress levels are measured based on the increase in nutrient uptake by plants as a mechanism of self-defense when under stress. A continuous electrical resistance measurement was used to estimate the rate of change of nutrients in agarose as the growth medium for Cicer arietinum (Chickpea) seeds. To determine the concentration of charge carriers in the growth medium, Drude's model was used. For identifying anomalies and forecasting plant stress, two experiments were conducted and outliers were found in electrical resistance and relative changes in carrier concentration. Anomaly in the first iteration was detected by applying k-Nearest Neighbour, One Class Support Vector Machine and Local Outlier Factor in unsupervised mode on electrical resistance data. In the second iteration, the neural network-based Long Short Term Memory method was used on the relative change in the carrier concentration data. As a result of the change in resistance of growth media during stress, nutrient concentrations shifted by 35%, as previously reported. Farmers who cater to small communities around them and are most affected by local and global stress factors can use this method of forecasting.

Role of structural specificity of ZnO particles in preserving functionality of proteins in their corona.

Reconfiguration of protein conformation in a micro and nano particle (MNP) protein corona due to interaction is an often-overlooked aspect in drug design and nano-medicine. Mostly, MNP-Protein corona studies focus on the toxicity of nano particles (NPs) in a biological environment to analyze biocompatibility. However, preserving functional specificity of proteins in an NP corona becomes critical for effective translation of nano-medicine. This paper investigates the non-classical interaction between insulin and ZnO MNPs using a classical electrical characterization technique at GHz frequency with an objective to understand the effect of the micro particle (MP) and nanoparticle (NP) morphology on the electrical characteristics of the MNP-Protein corona and therefore the conformation and functional specificity of protein. The MNP-Protein corona was subjected to thermal and enzymatic (papain) perturbation to study the denaturation of the protein. Experimental results demonstrate that the morphology of ZnO particles plays an important role in preserving the electrical characteristics of insulin.

Filaments and four ordered structures inside a neuron fire a thousand times faster than the membrane: theory and experiment.

The current action potential paradigm considers that all components beneath the neuron membrane are inconsequential. Filamentary communication is less known to the ionic signal transmission; recently, we have proposed that the two are intimately linked through time domains. We modified the atom probe-connected dielectric resonance scanner to operate in two-time domains, milliseconds and microseconds simultaneously for the first time. We resonate the ions for imaging rather than neutralizing them as patch clamps do; resonant transmission images the ion flow 103 times faster than the existing methods. We revisited action potential-related events by scanning in and around the axon initial segment (AIS). Four ordered structures in the cytoskeletal filaments exchange energy ~250 µs before a neuron fires, editing spike-time-gap-key to the brain's cognition. We could stop firing above a threshold or initiate a fire by wirelessly pumping electromagnetic signals. We theoretically built AIS, whose simulated electromagnetic energy exchange matched the experiment. Thus far, the scanner could detect & link uncorrelated biological events unfolding over 106 orders in the time scale simultaneously. Our experimental findings support a new dielectric resonator model of neuron functioning in various time domains, thus suggesting the dynamic anatomy of electrical activity as information-rich.