scholarly journals Temporal Patterns and Intra- and Inter-Cellular Variability in Carbon and Nitrogen Assimilation by the Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

2021 ◽  
Vol 12 ◽  
Author(s):  
Lubos Polerecky ◽  
Takako Masuda ◽  
Meri Eichner ◽  
Sophie Rabouille ◽  
Marie Vancová ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Population Heterogeneity ◽  
N Availability ◽  
Imaging Data ◽  
Turnover Rates ◽  
Unicellular Cyanobacterium ◽  
Phytoplankton Communities ◽  
N Assimilation ◽  
Wide Range ◽  
C And N

Unicellular nitrogen fixing cyanobacteria (UCYN) are abundant members of phytoplankton communities in a wide range of marine environments, including those with rapidly changing nitrogen (N) concentrations. We hypothesized that differences in N availability (N2 vs. combined N) would cause UCYN to shift strategies of intracellular N and C allocation. We used transmission electron microscopy and nanoscale secondary ion mass spectrometry imaging to track assimilation and intracellular allocation of 13C-labeled CO2 and 15N-labeled N2 or NO3 at different periods across a diel cycle in Cyanothece sp. ATCC 51142. We present new ideas on interpreting these imaging data, including the influences of pre-incubation cellular C and N contents and turnover rates of inclusion bodies. Within cultures growing diazotrophically, distinct subpopulations were detected that fixed N2 at night or in the morning. Additional significant within-population heterogeneity was likely caused by differences in the relative amounts of N assimilated into cyanophycin from sources external and internal to the cells. Whether growing on N2 or NO3, cells prioritized cyanophycin synthesis when N assimilation rates were highest. N assimilation in cells growing on NO3 switched from cyanophycin synthesis to protein synthesis, suggesting that once a cyanophycin quota is met, it is bypassed in favor of protein synthesis. Growth on NO3 also revealed that at night, there is a very low level of CO2 assimilation into polysaccharides simultaneous with their catabolism for protein synthesis. This study revealed multiple, detailed mechanisms underlying C and N management in Cyanothece that facilitate its success in dynamic aquatic environments.

scholarly journals Unsupervised content-preserving transformation for optical microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

scholarly journals Destruxin A Interacts with Aminoacyl tRNA Synthases in Bombyx mori

2021 ◽  
Vol 7 (8) ◽  
pp. 593
Author(s):  
Jingjing Wang ◽  
Alexander Berestetskiy ◽  
Qiongbo Hu
Keyword(s):  
Amino Acids ◽  
Protein Synthesis ◽  
Bombyx Mori ◽  
Metarhizium Anisopliae ◽  
Free Amino ◽  
Molecular Target ◽  
Trna Synthetases ◽  
Interaction Mode ◽  
Aminoacyl Trna Synthetases ◽  
Wide Range

Destruxin A (DA), a hexa-cyclodepsipeptidic mycotoxin produced by the entomopathogenic fungus Metarhizium anisopliae, exhibits insecticidal activities in a wide range of pests and is known as an innate immunity inhibitor. However, its mechanism of action requires further investigation. In this research, the interactions of DA with the six aminoacyl tRNA synthetases (ARSs) of Bombyx mori, BmAlaRS, BmCysRS, BmMetRS, BmValRS, BmIleRS, and BmGluProRS, were analyzed. The six ARSs were expressed and purified. The BLI (biolayer interferometry) results indicated that DA binds these ARSs with the affinity indices (KD) of 10−4 to 10−5 M. The molecular docking suggested a similar interaction mode of DA with ARSs, whereby DA settled into a pocket through hydrogen bonds with Asn, Arg, His, Lys, and Tyr of ARSs. Furthermore, DA treatments decreased the contents of soluble protein and free amino acids in Bm12 cells, which suggested that DA impedes protein synthesis. Lastly, the ARSs in Bm12 cells were all downregulated by DA stress. This study sheds light on exploring and answering the molecular target of DA against target insects.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

scholarly journals Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology

2001 ◽  
Vol 1 ◽  
pp. 767-776 ◽  
Author(s):  
E.D. Lund ◽  
M.C. Wolcott ◽  
G.P. Hanson
Keyword(s):  
Electrical Conductivity ◽  
Case Studies ◽  
Crop Yield ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Management Practices ◽  
N Availability ◽  
Soil Electrical Conductivity ◽  
Variable Yield ◽  
Wide Range

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

Relationships between C and N availability, substrate age, and natural abundance 13C and 15N signatures of soil microbial biomass in a semiarid climate

2009 ◽  
Vol 41 (8) ◽  
pp. 1605-1611 ◽  
Author(s):  
Jeff S. Coyle ◽  
Paul Dijkstra ◽  
Richard R. Doucett ◽  
Egbert Schwartz ◽  
Stephen C. Hart ◽  
...  
Keyword(s):  
Microbial Biomass ◽  
Soil Microbial Biomass ◽  
Natural Abundance ◽  
N Availability ◽  
Soil Microbial ◽  
Semiarid Climate ◽  
C And N ◽  
Substrate Age

scholarly journals Ferritin is associated with the severity of lung involvement but not with worse prognosis in patients with COVID-19: data from two Italian COVID-19 Units.

2020 ◽  
Author(s):  
Francesco Carubbi ◽  
Lia Salvati ◽  
Alessia Alunno ◽  
Fabio Maggi ◽  
Erika Borghi ◽  
...  
Keyword(s):  
Ct Scan ◽  
Time Course ◽  
Mediastinal Lymph Node ◽  
Clinical Signs ◽  
Pulmonary Involvement ◽  
Imaging Data ◽  
Age And Gender ◽  
Disease Outcomes ◽  
Wide Range ◽  
And Gender

Abstract The coronavirus 2019 disease (COVID-19) is characterised by a heterogeneous clinical presentation, a complex pathophysiology and a wide range of imaging findings, depending on disease severity and time course. We conducted a retrospective evaluation of hospitalized patients with proven SARS-CoV-2 infection, clinical signs of COVID-19 and computed tomography (CT) scan-proven pulmonary involvement, in order to identify relationships between clinical, serological, imaging data and disease outcomes in patients with COVID-19. Clinical and serological records of patients admitted to two COVID-19 Units of the Abruzzo region in Italy with proven SARS-CoV-2 pulmonary involvement investigated with CT scan, assessed at the time of admission to the hospital, were retrospectively evaluated.Sixty-one patients (22 females and 39 males) of median age 65 years were enrolled. Fifty-six patients were discharged while death occurred in 5 patients. None of the lung abnormalities detected by CT was different between discharged and deceased patients. No differences were observed in the features and extent of pulmonary involvement according to age and gender. Logistic regression analysis with age and gender as covariates demonstrated that ferritin levels over the 25th percentile were associated with the involvement of all 5 pulmonary lobes (OR=14.5, 95% CI=2.3-90.9, p=0.004), the presence of septal thickening (OR=8.2, 95% CI=1.6-40.9, p=0.011) and the presence of mediastinal lymph node enlargement (OR=12.0, 95% CI=1.1-127.5, p=0.039) independently of age and gender.We demonstrated that ferritin levels over the 25th percentile are associated with a more severe pulmonary involvement, independently of age and gender and not associated with disease outcomes. The identification of reliable biomarkers in patients with COVID-19 may help guiding clinical decision, tailoring therapeutic approaches and ultimately improving the care and prognosis of patients with this disease.

scholarly journals Microstructural Integrity of White Matter Moderates an Association Between Childhood Adversity and Adult Trait Anger

2018 ◽  
Author(s):  
M. Justin Kim ◽  
Maxwell L. Elliott ◽  
Tracy C. d’Arbeloff ◽  
Annchen R. Knodt ◽  
Spenser R. Radtke ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
White Matter ◽  
Negative Emotion ◽  
Childhood Adversity ◽  
Trait Anger ◽  
Uncinate Fasciculus ◽  
Imaging Data ◽  
Wide Range ◽  
Microstructural Integrity

AbstractAmongst a number of negative life sequelae associated with childhood adversity is the later expression of a higher dispositional tendency to experience anger and frustration to a wide range of situations (i.e., trait anger). We recently reported that an association between childhood adversity and trait anger is moderated by individual differences in both threat-related amygdala activity and executive control-related dorsolateral prefrontal cortex (dlPFC) activity, wherein individuals with relatively low amygdala and high dlPFC activity do not express higher trait anger even when having experienced childhood adversity. Here, we examine possible structural correlates of this functional dynamic using diffusion magnetic resonance imaging data from 647 young adult men and women volunteers. Specifically, we tested whether the degree of white matter microstructural integrity as indexed by fractional anisotropy modulated the association between childhood adversity and trait anger. Our analyses revealed that higher microstructural integrity of multiple pathways was associated with an attenuated link between childhood adversity and adult trait anger. Amongst these pathways was the uncinate fasciculus, which not only provides a major anatomical link between the amygdala and prefrontal cortex but also is associated with individual differences in regulating negative emotion through top-down cognitive reappraisal. These findings suggest that higher microstructural integrity of distributed white matter pathways including but not limited to the uncinate fasciculus may represent an anatomical foundation serving to buffer against the expression of childhood adversity as later trait anger, which is itself associated with multiple negative health outcomes.

scholarly journals Deep learning classifiers for near infrared spectral imaging: a tutorial

2020 ◽  
Author(s):  
Jun-Li Xu ◽  
Cecilia Riccioli ◽  
Ana Herrero-Langreo ◽  
Aoife Gowen
Keyword(s):  
Deep Learning ◽  
Visual Recognition ◽  
Near Infrared ◽  
Three Dimensional ◽  
Spectral Imaging ◽  
Learning Approaches ◽  
Imaging Data ◽  
Spectral Image ◽  
Background Removal ◽  
Wide Range

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

scholarly journals Mycorrhizal Communities and Isotope Signatures in Two Partially Mycoheterotrophic Orchids

2021 ◽  
Vol 12 ◽  
Author(s):  
Hans Jacquemyn ◽  
Rein Brys ◽  
Michael Waud ◽  
Alexandra Evans ◽  
Tomáš Figura ◽  
...  
Keyword(s):  
Isotopic Signatures ◽  
Carbon And Nitrogen ◽  
Wide Range ◽  
Terrestrial Orchids ◽  
Significant Enrichment ◽  
Mycoheterotrophic Plants ◽  
C And N ◽  
Species Specific ◽  
Epipactis Helleborine ◽  
Isotope Analyses

Partial mycoheterotrophy, the ability of plants to obtain carbon from fungi throughout their life cycle in combination with photosynthesis, appears to be more common within the Plant Kingdom than previously anticipated. Recent studies using stable isotope analyses have indicated that isotope signatures in partially mycoheterotrophic plants vary widely among species, but the relative contributions of family- or species-specific characteristics and the identity of the fungal symbionts to the observed differences remain unclear. Here, we investigated in detail mycorrhizal communities and isotopic signatures in four co-occurring terrestrial orchids (Platanthera chlorantha, Epipactis helleborine, E. neglecta and the mycoheterotrophic Neottia nidus-avis). All investigated species were mycorrhizal generalists (i.e., associated with a large number of fungi simultaneously), but mycorrhizal communities differed significantly between species. Mycorrhizal communities associating with the two Epipactis species consisted of a wide range of fungi belonging to different families, whereas P. chlorantha and N. nidus-avis associated mainly with Ceratobasidiaceae and Sebacinaceae species, respectively. Isotopic signatures differed significantly between both Epipactis species, with E. helleborine showing near autotrophic behavior and E. neglecta showing significant enrichment in both carbon and nitrogen. No significant differences in photosynthesis and stomatal conductance were observed between the two partially mycoheterotrophic orchids, despite significant differences in isotopic signatures. Our results demonstrate that partially mycoheterotrophic orchids of the genus Epipactis formed mycorrhizas with a wide diversity of fungi from different fungal families, but variation in mycorrhizal community composition was not related to isotope signatures and thus transfer of C and N to the plant. We conclude that the observed differences in isotope signatures between E. helleborine and E. neglecta cannot solely be explained by differences in mycorrhizal communities, but most likely reflect a combination of inherent physiological differences and differences in mycorrhizal communities.

scholarly journals Cell-free genetic devices confer autonomic and adaptive properties to hydrogels

2019 ◽  
Author(s):  
Colette J. Whitfield ◽  
Alice M. Banks ◽  
Gema Dura ◽  
John Love ◽  
Jonathan E. Fieldsend ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Information Processing ◽  
Synthetic Biology ◽  
Smart Materials ◽  
Living Organism ◽  
Chemical Diversity ◽  
Biological Information ◽  
Self Healing ◽  
Wide Range

AbstractSmart materials are able to alter one or more of their properties in response to defined stimuli. Our ability to design and create such materials, however, does not match the diversity and specificity of responses seen within the biological domain. We propose that relocation of molecular phenomena from living cells into hydrogels can be used to confer smart functionality to materials. We establish that cell-free protein synthesis can be conducted in agarose hydrogels, that gene expression occurs throughout the material and that co-expression of genes is possible. We demonstrate that gene expression can be controlled transcriptionally (using in gel gene interactions) and translationally in response to small molecule and nucleic acid triggers. We use this system to design and build a genetic device that can alter the structural property of its chassis material in response to exogenous stimuli. Importantly, we establish that a wide range of hydrogels are appropriate chassis for cell-free synthetic biology, meaning a designer may alter both the genetic and hydrogel components according to the requirements of a given application. We probe the relationship between the physical structure of the gel and in gel protein synthesis and reveal that the material itself may act as a macromolecular crowder enhancing protein synthesis. Given the extensive range of genetically encoded information processing networks in the living kingdom and the structural and chemical diversity of hydrogels, this work establishes a model by which cell-free synthetic biology can be used to create autonomic and adaptive materials.Significance statementSmart materials have the ability to change one or more of their properties (e.g. structure, shape or function) in response to specific triggers. They have applications ranging from light-sensitive sunglasses and drug delivery systems to shape-memory alloys and self-healing coatings. The ability to programme such materials, however, is basic compared to the ability of a living organism to observe, understand and respond to its environment. Here we demonstrate the relocation of biological information processing systems from cells to materials. We achieved this by operating small, programmable genetic devices outside the confines of a living cell and inside hydrogel matrices. These results establish a method for developing materials functionally enhanced with molecular machinery from biological systems.

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