Identification and characterization of intact proteins in complex mixturesExpedeon
The ability to fully characterize proteins in their intact forms allows thorough biological investigation of the functional importance of changes such as post-translational modifications, protein isoforms/sequence variations, and protease cleavages.
Brief explanation of what is PET, the main components for a PET system along with their basic functions. The principle behind PET inclusive of positron emission and emission detection. Acquisition and reconstruction of the collected data to produce the final image. Finally the pros and cons of Positron emission tomography.
Identification and characterization of intact proteins in complex mixturesExpedeon
The ability to fully characterize proteins in their intact forms allows thorough biological investigation of the functional importance of changes such as post-translational modifications, protein isoforms/sequence variations, and protease cleavages.
Brief explanation of what is PET, the main components for a PET system along with their basic functions. The principle behind PET inclusive of positron emission and emission detection. Acquisition and reconstruction of the collected data to produce the final image. Finally the pros and cons of Positron emission tomography.
Unsupervised Deconvolution Neural Network for High Quality Ultrasound ImagingShujaat Khan
High quality US imaging demand large number of measurements that can increase the cost, size and power requirements. Therefore, low-powered, portable and 3D ultrasound imaging system require reconstruction algorithms that can produce high quality images using fewer receive measurements. Number of model specific methods has been proposed which doesn't work under perturbation. For instance, compressive deconvolution ultrasound which provide a reasonable quality with limited measurements however, it has its own down-sides such as high computation cost and accurate estimation of point spread function (PSF). An other major limitation of conventional methods is that they require RF or base-band signal which is difficult to obtain from portable US systems. To deal with the aforementioned issues, in this study we designed a novel deep deconvolution model for image domain-based deconvolution. The proposed deep deconvolution (DeepDeconv) model can be trained in an unsupervised fashion, alleviate the need of paired high and low quality images. The model was evaluated on both the phantom and in-vivo scans for various sampling configurations. The proposed DeepDeconv significantly enhance the details of anatomical structures and using unsupervised learning on average it achieved 2.14dB, 4.96dB and 0.01 units gain in CR, PSNR and SSIM values respectively, which are comparable to the supervised method.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
Protein structure determinationand our software toolsMark Berjanskii
Protein structure determinationand our software tools. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, mark berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, Wishart group, hybrid data, X-ray crystallography, CryoEM, Mass Spectrometry
Protein structure determination from hybrid NMR data.Mark Berjanskii
Protein structure determination from hybrid NMR data. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, mark berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, Wishart group, hybrid data, SAXS, WAXS, X-ray crystallography, FRET, CryoEM, EPR, Mass Spectrometry
Dr. Lawrence Yip explained how Photoacoustic (PA) imaging works, where it fits in with other modalities and, how your research could benefit from this emerging technology.
Excellent spatial resolution, depth penetration, and superior contrast are just some of the advantages often associated with PA imaging. In this webinar, we dove into the advantages, where they can be beneficial, and how the TriTom’s patented technology overcomes some of the challenges experienced by early adopters of this imaging modality.
The TriTom is a turnkey, compact, tabletop imaging system that combines the sensitivity of fluorescence molecular tomography with the depth penetration and spatial resolution of PA tomography. Many applications including cancer, neuroimaging, developmental biology, and cardiovascular research could benefit from adding these imaging modalities, and we will draw from literature and concrete examples to demonstrate this advantage.
High Precision And Fast Functional Mapping Of Cortical Circuitry Through A No...Taruna Ikrar
Taruna Ikrar, MD., PhD. Author at (High Precision and Fast Functional Mapping of Cortical Circuitry Through a Novel Combination of Voltage Sensitive Dye Imaging and Laser Scanning Photostimulation)
Electrophysiological imaging for advanced pharmacological screening3Brain AG
We at 3Brain are committed to advancing scientific research and boosting drug discovery. Like our technology, our product lines are always evolving to accommodate high-resolution recording of in vitro cultures. Discover our HD-MEA technology and soon-to-be-released devices and see how they are furthering research in brain diseases, drug discovery, retinal organoids, etc..
For more information, visit our website at https://www.3brain.com
Similar to Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury - by Jahandar Jahanipour
Unsupervised Deconvolution Neural Network for High Quality Ultrasound ImagingShujaat Khan
High quality US imaging demand large number of measurements that can increase the cost, size and power requirements. Therefore, low-powered, portable and 3D ultrasound imaging system require reconstruction algorithms that can produce high quality images using fewer receive measurements. Number of model specific methods has been proposed which doesn't work under perturbation. For instance, compressive deconvolution ultrasound which provide a reasonable quality with limited measurements however, it has its own down-sides such as high computation cost and accurate estimation of point spread function (PSF). An other major limitation of conventional methods is that they require RF or base-band signal which is difficult to obtain from portable US systems. To deal with the aforementioned issues, in this study we designed a novel deep deconvolution model for image domain-based deconvolution. The proposed deep deconvolution (DeepDeconv) model can be trained in an unsupervised fashion, alleviate the need of paired high and low quality images. The model was evaluated on both the phantom and in-vivo scans for various sampling configurations. The proposed DeepDeconv significantly enhance the details of anatomical structures and using unsupervised learning on average it achieved 2.14dB, 4.96dB and 0.01 units gain in CR, PSNR and SSIM values respectively, which are comparable to the supervised method.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
Protein structure determinationand our software toolsMark Berjanskii
Protein structure determinationand our software tools. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, mark berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, Wishart group, hybrid data, X-ray crystallography, CryoEM, Mass Spectrometry
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Protein structure determination from hybrid NMR data. Presentation is related to: biochemistry, bioinformatics, biology, biophysics, mark berjanskii, molecular biology, molecular dynamics, molecular modeling, nmr spectroscopy, protein nmr, public speaking, python programming, sparse data, structural biology, structure determination, teaching, web design, web development, web programming, Wishart group, hybrid data, SAXS, WAXS, X-ray crystallography, FRET, CryoEM, EPR, Mass Spectrometry
Dr. Lawrence Yip explained how Photoacoustic (PA) imaging works, where it fits in with other modalities and, how your research could benefit from this emerging technology.
Excellent spatial resolution, depth penetration, and superior contrast are just some of the advantages often associated with PA imaging. In this webinar, we dove into the advantages, where they can be beneficial, and how the TriTom’s patented technology overcomes some of the challenges experienced by early adopters of this imaging modality.
The TriTom is a turnkey, compact, tabletop imaging system that combines the sensitivity of fluorescence molecular tomography with the depth penetration and spatial resolution of PA tomography. Many applications including cancer, neuroimaging, developmental biology, and cardiovascular research could benefit from adding these imaging modalities, and we will draw from literature and concrete examples to demonstrate this advantage.
High Precision And Fast Functional Mapping Of Cortical Circuitry Through A No...Taruna Ikrar
Taruna Ikrar, MD., PhD. Author at (High Precision and Fast Functional Mapping of Cortical Circuitry Through a Novel Combination of Voltage Sensitive Dye Imaging and Laser Scanning Photostimulation)
Electrophysiological imaging for advanced pharmacological screening3Brain AG
We at 3Brain are committed to advancing scientific research and boosting drug discovery. Like our technology, our product lines are always evolving to accommodate high-resolution recording of in vitro cultures. Discover our HD-MEA technology and soon-to-be-released devices and see how they are furthering research in brain diseases, drug discovery, retinal organoids, etc..
For more information, visit our website at https://www.3brain.com
Similar to Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury - by Jahandar Jahanipour (20)
Houston machine learning meetup, two papers are discussed:
- A Contextual-Bandit Approach to Personalized News Article Recommendation
http://rob.schapire.net/papers/www10.pdf
- An efficient bandit algorithm for realtime multivariate optimization
https://www.kdd.org/kdd2017/papers/view/an-efficient-bandit-algorithm-for-realtime-multivariate-optimization
Sr. Architect Pradeep Reddy, from Qubole, presents the state of Data Science in the enterprise industries today, followed by deep dive of an end-to-end real world machine learning use case. We'll explore the best practices and challenges of big data operations when developing new machine learning features and advanced analytics products at scale in the cloud.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
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Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain Rat Slices After Traumatic Brain Injury - by Jahandar Jahanipour
1. Jahandar Jahanipour
Department of Electrical and Computer
Engineering
University of Houston, TX
Deep Hierarchical Profiling & Pattern Discovery:
Application to Whole Brain Rat Slices After Traumatic
Brain Injury
Advisor:
Prof. Badrinath Roysam
2. Introduction
Concussion: disruption in the normal
function of the brain caused by a bump,
blow, …
Headache
Dizziness
Depression
Chris
Henry
John Grimsley
2
1.5 atm Fluid percussion injury multiplexed imaging technique Whole brain rat slice
~ 300,000 cells
~ 3Gb
~32,000 ×47,000 pixels
Motivation: Computational data-driven image interpretation on large datasets
GOAL:
Profile tissue alterations in a manner that
is comprehensive , quantitative and
sensitive to multiple types of changes.
3. Deep Feature Extraction
Nuclear morphological features
are not able to capture
thorough molecular signature.
Associative features are
dependent on nuclear
segmentation of object.
3
NeuN+
NeuN-
40µm
NeuN
DAPI+Histones
Conventional Cytometric Features:
Deep Features:
Nuclear segmentation of cells using DAPI +Histone channels.
Scattering network formed by wavelet-modulus
cascading.
Deep features capture
basal cell morphology and
molecular distribution,
JOINTLY.
4. Can We Perform Computationally Guided
Biological Interpretation?
Can we profile heterogeneity among cells?
4
S100 GFAP
200 µm
20µm
20µm
20µm
Is there any relation between activation level of a cell to the location of the
cell from the center of the injury?
6. Profiling Heterogeneity Within Same Structure
6
Astrocyte
Classification
Biomarkers for astrocyte phenotyping
S100 GFAP GLAST
Resting Astrocytes All (+) Subset (low) Subset (+)
Reactive Astrocytes All (+) All (high) All (+)
S100 GFAP
200 µm
S100-
S100+
S100+
S100-
All cells
Classifying astrocytes
within cortex
Astrocyte can be reconstructed using S100 and GFAP biomarkers for further analysis.
Li+VPa (treatment)
7. Profiling Cell Status Activation
7
S100 GFAP
200 µm
20µm 20µm20µm
S100+S100-
All cells
reactive resting
highmoderate
Astrocyte
Classification
Biomarkers for astrocyte
phenotyping
S100 GFAP GLAST
Resting Astrocytes All (+)
Subset
(low)
Subset (+)
Reactive
Astrocytes
All (+) All (high) All (+)
Moderately active
astrocytes
Very active
astrocytes
Profiling of astrocytes’ activation status reveals the relation of each
cell’s location relative to the site of injury.
Distance to the injury activation
Li+VPa (treatment)
8. Profiling Heterogeneity Within Same Structure
8
200 µm
group 1
group 2
group 3
group 4
group 5
Oligo-glial markers
S100 APC MBP PLP
Laminar-like pattern
resembling cortical
neuronal layers using
oligo-glial biomarkers:
Identifying 5
different cell
subpopulations
organized in
cortical layer
fashion
Profiling of oligo-glial biomarkers to discriminate cortical layers.
LiVPa