scholarly journals Geoelectric prospecting in University Campus region for detection of possible geological discontinuities, Rio, Patra, Greece.

2016 ◽  
Vol 47 (3) ◽  
pp. 1042
Author(s):  
L. G. Angelis ◽  
P. Stephanopoulos ◽  
P. St. Papamarinopoulos
Keyword(s):  
Physical Property ◽  
Spectral Ratio ◽  
Horizontal Layer ◽  
Imaging Data ◽  
Geophysical Investigation ◽  
Mapping Procedure ◽  
Main Technique ◽  
Wide Range ◽  
Geological Discontinuity ◽  
Geological Discontinuities

Geophysical prospecting is a non catastrophic technique, applicable on a wide range of problems, including archaeological, environmental and geological problems. At Campus University of Patras, a detailed geophysical investigation applied for detection of possible existing geological discontinuities, which produced serious problems at buildings and main roads of Campus. As main technique used the electric mapping and electric imaging. These were applied on already prepared geophysical grids by measuring parallel profiles along and perpendicular to the geomagnetic north. The two geophysical grids were separated 100 meters away each other. Firstly, an electric mapping procedure took place by using twin-probe array with four electrodes in distance between 0.5-3 meters. As result was the recording of soil resistance on horizontal layer with constant depth. By processing the data through Geosoft Oasis Montaj software, the distribution of this physical property was illustrated on color scale maps. Secondly, electric imaging technique applied with twenty-five equal space electrodes along straight lines, with one meter space byusing the hybrid arrangement Wenner-Schlumberger (Stephanopoulos, 2002). As result was the recording of distribution of soil apparent resistivity on a vertical layer in eight separated depths. Resistivity calculated by processing imaging data through 2D mathematical algorithm based on least squares inversion (Res2Dinv). Furtherprocessing by Oasis Montaj, had as a result the production of horizontal slices (Stephanopoulos 2002) and 3D maps, where the resistivity distribution was illustrated on separated depths, in color and grey schedule format. The combined geophysical investigation with the contribution of the HVSR (Horizontal to Vertical Spectral Ratio) technique confirmed the existence of geological discontinuity.

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.

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 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 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 Field and Laboratory-based Approach for the Determination of Friction Angle of Geological Discontinuities of Malaysian Granites

2011 ◽  
Vol 28 (2) ◽  
pp. 151 ◽  
Author(s):  
R. A Ghani ◽  
T. L Goh ◽  
A. M Hariri ◽  
Y. N Baizura
Keyword(s):  
Friction Angle ◽  
Roughness Coefficient ◽  
Average Value ◽  
Peak Friction Angle ◽  
Maximum Value ◽  
Wide Range ◽  
Discontinuity Surfaces ◽  
Geological Discontinuities ◽  
Range Of Values

The basic friction angle, Φb for artificially sawn discontinuity planes for fresh granite, as determined by tilt testing, has an average value of 30º. For the natural rough discontinuity surfaces, a wide range of values have been determined for the peak friction angle, Φpeak ranging from 47º to a maximum value of 80º, depending on the joint roughness coefficient (JRC). The average values of the friction angles for the different degrees of roughness were as follows: JRC 2–4 = 58°; JRC 6–8 = 60°; JRC 8–10 = 47°; JRC 12–14 = 60°; JRC 14–16 = 71° ; JRC 18–20 = 80°.

scholarly journals Global whole-rock geochemical database compilation

2019 ◽  
Author(s):  
Matthew Gard ◽  
Derrick Hasterok ◽  
Jacqueline Halpin
Keyword(s):  
Database Management ◽  
Temporal Trends ◽  
Physical Property ◽  
Database Management System ◽  
Geochemical Data ◽  
Data Sets ◽  
Unique Contribution ◽  
Data Set ◽  
Wide Range ◽  
Geochemical Indices

Abstract. Dissemination and collation of geochemical data are critical to promote rapid, creative and accurate research and place new results in an appropriate global context. To this end, we have assembled a global whole-rock geochemical database, with other associated sample information and properties, sourced from various existing databases and supplemented with numerous individual publications and corrections. Currently the database stands at 1,023,490 samples with varying amounts of associated information including major and trace element concentrations, isotopic ratios, and location data. The distribution both spatially and temporally is quite heterogeneous, however temporal distributions are enhanced over some previous database compilations, particularly in terms of ages older than ~ 1000 Ma. Also included are a wide range of computed geochemical indices, physical property estimates and naming schema on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated by sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data is published in .csv format for the purposes of simple distribution but exists in a format acceptable for database management systems (e.g. SQL). One can either manipulate this data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload to a relational database management system for easy querying and management of the data as unique keys already exist. This data set will continue to grow, and we encourage readers to contact us or other database compilations contained within about any data that is yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592823 (Gard et al., 2019).

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 Ultra-fast accurate reconstruction of spiking activity from calcium imaging data

2018 ◽  
Vol 119 (5) ◽  
pp. 1863-1878 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Calcium Imaging ◽  
Reconstruction Method ◽  
Imaging Data ◽  
Decay Kinetics ◽  
Experimental Conditions ◽  
Model Free ◽  
Reconstruction Methods ◽  
Wide Range ◽  
Fast Processing ◽  
Spiking Activity

Calcium imaging provides an indirect observation of the underlying neural dynamics and enables the functional analysis of neuronal populations. However, the recorded fluorescence traces are temporally smeared, thus making the reconstruction of exact spiking activity challenging. Most of the established methods to tackle this issue are limited in dealing with issues such as the variability in the kinetics of fluorescence transients, fast processing of long-term data, high firing rates, and measurement noise. We propose a novel, heuristic reconstruction method to overcome these limitations. By using both synthetic and experimental data, we demonstrate the four main features of this method: 1) it accurately reconstructs both isolated spikes and within-burst spikes, and the spike count per fluorescence transient, from a given noisy fluorescence trace; 2) it performs the reconstruction of a trace extracted from 1,000,000 frames in less than 2 s; 3) it adapts to transients with different rise and decay kinetics or amplitudes, both within and across single neurons; and 4) it has only one key parameter, which we will show can be set in a nearly automatic way to an approximately optimal value. Furthermore, we demonstrate the ability of the method to effectively correct for fast and rather complex, slowly varying drifts as frequently observed in in vivo data. NEW & NOTEWORTHY Reconstruction of spiking activities from calcium imaging data remains challenging. Most of the established reconstruction methods not only have limitations in adapting to systematic variations in the data and fast processing of large amounts of data, but their results also depend on the user’s experience. To overcome these limitations, we present a novel, heuristic model-free-type method that enables an ultra-fast, accurate, near-automatic reconstruction from data recorded under a wide range of experimental conditions.

scholarly journals Computer Simulation of Metal Ions Transport to Uneven Substrates during Ionized Plasma Vapour Deposition

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Tomáš Ibehej ◽  
Jakub Hromádka ◽  
Rudolf Hrach
Keyword(s):  
Vapour Deposition ◽  
Copper Ions ◽  
Computational Study ◽  
Ion Fluxes ◽  
Particle Simulation ◽  
Plasma Properties ◽  
Local Maxima ◽  
Sheath Region ◽  
Main Technique ◽  
Wide Range

We present a computational study of processes taking place in a sheath region formed near a negatively biased uneven substrate during ionized plasma vapour deposition. The sputtered metal atoms are ionized on their way to substrate and they are accelerated in the sheath near the substrate. They are able to penetrate to high-aspect-ratio structures, for example, trenches, which can be, therefore, effectively coated. The main technique used was a two-dimensional particle simulation. The results of our model predict the energy and angular distributions of impinging ions in low-pressure conditions which are characteristic for this method and where typical continuous models fail due to unfulfilled assumptions. Input bulk plasma properties were computed by a “zero dimensional” global model which took into account more physical processes important on a scale of the whole magnetron chamber. Output parameters, such as electrostatic potential, energy of ions, and ion fluxes, were computed for wide range of conditions (electron density and substrate bias) to show the influence of these conditions on observed phenomena, penetration of sheath inside the trench, deceleration of argon and copper ions inside the trench, and local maxima of ion fluxes near the trench opening.

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