Biology subjects

Ebrahimi, A.

Publications and source records attributed to Ebrahimi, A..

24 records · Page 2Linked to original sources

bioRxiv2021

Particle foraging strategies promote microbial diversity in marine environments

Microbial foraging in patchy environments, where resources are fragmented into particles or pockets embedded in a large matrix, plays a key role in natural environments. In the oceans and freshwater systems, particle-associated bacteria can interact with particle surfaces in different ways: some colonize only during short transients, while others form long-lived, stable colonies. We do not yet understand the ecological mechanisms by which both short-term and long-term colonizers can coexist. Here, we address this problem with a mathematical model that explains how marine populations with different detachment rates from particles can stably coexist. In our model, populations grow only while on particles, but also face the increased risk of mortality by predation and sinking. Key to coexistence is the idea that detachment from particles modulates both net growth and mortality, but in opposite directions, creating a trade-off between them. While slow-detaching populations show the highest growth return (i.e., produce more net offspring), they are more susceptible to suffer higher rates of mortality than fast-detaching populations. Surprisingly, fluctuating environments, manifesting as blooms of particles (favoring growth) and predators (favoring mortality) significantly expand the likelihood that populations with different detachment rates can coexist. Our study shows how the spatial ecology of microbes in the ocean can lead to a predictable diversification of foraging strategies and the coexistence of multiple taxa on a single growth-limiting resource.

ecology↗

bioRxiv2021

Depth Dataset Using Microsoft Kinect-v2

In biomedical imaging studies, numerous methods have been used to capture human data, mostly by using magnetic resonance imaging (MRI) and computed tomography (CT). However, due to being inexpensive and accessibility of Microsoft Kinect, its usage has been significantly increased in recent years. In this study, we aimed to represent the procedure of data acquisition, which includes a set of depth images from individuals back surfaces. The goal of image acquisition is to investigate spinal deformities and landmark detection of the back surface. Traditional imaging systems are challenging, most notably because of ionized beams in the data acquisition process, which has not been solved yet. Indeed, noninvasiveness is the most crucial advantage of our study. Our imaging system was set in a dim laboratory, and the University approved the ethical letter of Medical Sciences before data acquisition. After that, the subjects (total 105; 50 women and 55 men) were recruited, and data images were captured from the back surface. Then, we increased the imaging data size by using the augmentation method to use deep learning methods in future works. Finally, this Dataset leads us to the desired output in our study procedure.

bioengineering↗

bioRxiv2021

Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities that cause physical dysfunction and correspondingly reduce peoples quality of life. Radiography is the most common method for detecting these deformities and requires monitoring and follow-up until full treatment; however, it frequently exposes the patient to X-rays and ionization and as a result, cancer risk is increased in the patient and could be highly dangerous for children or pregnant women. To prevent this, several solutions have been proposed using topographic data analysis of the human back surface. The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and practical information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach - Deep convolution neural network-was used as a model to estimate the location of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed that 86.9% of PDJ and 80% of PCK. According to the results, this study was more effective than other methods with more than thousands of training data.

bioengineering↗

bioRxiv2020

Dynamic Laser Speckle Imaging meets Machine Learning to enable Rapid Antibacterial Susceptibility Testing (DyRAST)

Rapid antibacterial susceptibility testing (RAST) methods which measure change of a bacterial phenotype in response to a given treatment are of significant importance in healthcare, as they can assist care-givers in timely administration of the right treatment. Various RAST techniques have been reported for tracking bacterial phenotypes, such as size, shape, motion, and metabolic activity. However, they still require bulky and expensive instruments (which hinders their application in resource-limited environments) and/or utilize labeling reagents (which can interfere with antibiotics and add to cost). Furthermore, the existing ultra-rapid methods do not address possible adaptation of gradual adaptation of bacteria to antibiotics, which can lead to false interpretation of resistance when using ultra-rapid methods. In this work, we present a RAST approach leveraging machine learning analysis of time-resolved dynamic laser speckle imaging (DLSI) results to accurately predict the minimum inhibitory concentration (MIC) of a model strain of Escherichia coli in 60 minutes, compared to 6 hours using the currently FDA-approved phenotype-based RAST technique. To demonstrate the DLSI performance, we studied the effect of a {beta}-lactam ampicillin and an aminoglycoside gentamicin on Escherichia coli strain K-12. DLSI captures change of bacterial motion/division in response to treatment. The machine learning algorithm was trained and validated using the overnight results of gold standard, broth microdilution method. Empowered by machine learning, DyRAST can predict MIC with high accuracy comparable to gold standard methods through a voting strategy.

microbiology↗

bioRxiv2020

The complete chloroplast genome sequence of American elm (Ulmus americana) and comparative genomics of related species

American elm, Ulmus americana L., was cultivated widely in USA and Canada as a landscape tree, but the genome of this important species is poorly characterized. For the first time, we describe the sequencing and assembly of the chloroplast genomes of two American elm genotypes (RV16 and Am57845). The complete chloroplast genome of U. americana ranged from 158,935-158,993 bp. The genome contains 127 genes, including 85 protein-coding genes, 34 tRNA genes and 8 rRNA genes. Between the two American elm chloroplasts we sequenced, we identified 240 sequence variants (SNPs and indels). To evaluate the phylogeny of American elm, we compared the chloroplast genomes of two American elms along with seven Asian elm species and twelve other chloroplast genomes available through the NCBI database. As expected, Ulmus was closely related to Morus and Cannabis, as all three genera are assigned to the Urticales. Comparison of American elm with Asian elms revealed that trnH was absent from the chloroplast of American elm but not most Asian elms; conversely, petB, petD, psbL, trnK, and rps16 are present in the American elm but absent from all Asian elms. The complete chloroplast genome of U. americana will provide useful genetic resources for characterizing the genetic diversity of U. americana and potentially help to conserve natural populations of American elm.

genomics↗

bioRxiv2019

Cooperation and spatial self-organization determine ecosystem function for polysaccharide-degrading bacteria

The recycling of particulate organic matter (POM) by microbes is a key part of the global carbon cycle, one which is mediated by the extracellular hydrolysis of polysaccharides and the production of public goods that can trigger social behaviors in bacteria. Despite the potential importance of these microbial interactions, their role in regulating of ecosystem function remains unclear. In this study, we developed a computational and experimental model system to address this challenge and studied how POM depolymerization rate and its uptake efficiency -two main ecosystem function parameters- depended on social interactions and spatial self-organization on particle surfaces. We found an emergent trade-off between rate and efficiency resulting from the competition between oligosaccharide diffusion and cellular uptake, with low rate and high efficiency being achieved through cell-to-cell cooperation between degraders. Bacteria cooperated by aggregating in cell-clusters of ~10-20m, where cells were able to share public goods. This phenomenon, which was independent of any explicit group-level regulation, led to the emergence of critical cell concentrations below which degradation did not occur, despite all resources being available in excess. By contrast, when particles were labile and turnover rates were high, aggregation promoted competition and decreased the efficiency of carbon utilization. Our study shows how social interactions and cell aggregation determine the rate and efficiency of particulate carbon turnover in environmentally relevant scenarios.\n\nSignificance StatementMicroorganisms can cooperate by secreting public goods that benefit local neighbors, however, the impact of cooperation on ecosystem functions remains poorly constrained. We here pair computation and experiment to show that bacterial cooperation mediates the degradation of polysaccharide particles recalcitrant to hydrolysis in aquatic environments. On particle surfaces, cooperation emerges through the self-organization of cells into ~10-20m clusters that promote cooperative uptake of hydrolysis products. The transition between cooperation and competition in aggregates is mitigated by individual cell behaviors such as motility and chemotaxis, that promote reorganization on the particle surface. When cooperation is required, the degradation of recalcitrant biopolymers can only take place when degraders exceed a critical cell concentration, underscoring the importance of microbial interactions for ecosystem function.

ecology↗