fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
Event Related Potentials, Cognitive Evoked Potentials. These are stimulus unrelated potentials, which depend on the patient's ability to differentiate between a rare stimulus and a common stimulus.
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
Event Related Potentials, Cognitive Evoked Potentials. These are stimulus unrelated potentials, which depend on the patient's ability to differentiate between a rare stimulus and a common stimulus.
This presentation reviews the common artifacts in EEG, their identification and rectification. Examples of various artifacts are provided in the presentation.
Normal EEG patterns, frequencies, as well as patterns that may simulate diseaseRahul Kumar
This presentation discusses the vast range of traces that show the variations in normal EEG patterns, as well as discussing the frequency and amplitudes of various normal waveforms.
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
High-intensity LEDs are embedded in the flash stimulation pad
The small disc shape and silicone properties of the pad make it both flexible and lightweight
Illuminance can be set up to 20,000 lux, and different light emission times and cycles can be chosen.
A common system for placing electrodes is the “10-20 International System” which is based on measurements of head size (Jasper, 1958).
The mid-occipital electrode location (OZ) is on the midline.
The distance above the inion calculated as 10 % of the distance between the inion and nasion, which is 3-4 cm in most adults
Lateral occipital electrodes are a similar distance off the midline.
To have reliable VEPs, Intraoperatively, the following factors are important
Maintaining normal intraoperative physiological/hemodynamic parameters
Use of TIVA instead of inhalational anesthesia
Better stimulus delivery methods
Recording intraoperative ERG to ensure good retinal stimulation and
Employing optimal recording parameters
This presentation looks at EEG signal generation, pyramidal cells, recording of EEG, source localisation, polarity, analysis of dipole, derivations, montages,
ECochG is a variant of brainstem audio evoked response (ABR) where the recording electrode is placed as close as practical to the cochlea. We will use the abbreviation ECOG and ECochG interchangeably below. ECOG is preferable to us as it is shorter.
ECOG is intended to diagnose Meniere's disease, and particular, hydrops (swelling of the inner ear). ECOG may also be abnormal in perilymph fistula, and in superior canal dehiscence. The common feature connecting these illnesses is an imbalance in pressure between the endolymphatic and perilymphatic compartment of the inner ear.
ECOG can also be used to show that the cochlea is normal, in persons who are deaf. The cochlear microphonic of ECOG may be normal in auditory neuropathy (Santarelli and Arslan 2002) as well as other disorders in which the cochlea is preserved but the auditory nerve is damaged (Yokoyama, Nishida et al. 1999).
Finally, ECOG's have also been used to as a indicator of the temporary threshold shift that may follow noise injury (Nam et al, 2004).
MagnetoenCephaloGraphy (MEG) is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.
CNIM Questions related to Mathematics and Formulas Anurag Tewari MD
There are a few questions in CNIM exam that would require you to use your knowledge of simple mathematics to derive to an answer. Here are a few representative questions. Please do read more and practice as many questions as you can.
This presentation reviews the common artifacts in EEG, their identification and rectification. Examples of various artifacts are provided in the presentation.
Normal EEG patterns, frequencies, as well as patterns that may simulate diseaseRahul Kumar
This presentation discusses the vast range of traces that show the variations in normal EEG patterns, as well as discussing the frequency and amplitudes of various normal waveforms.
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
High-intensity LEDs are embedded in the flash stimulation pad
The small disc shape and silicone properties of the pad make it both flexible and lightweight
Illuminance can be set up to 20,000 lux, and different light emission times and cycles can be chosen.
A common system for placing electrodes is the “10-20 International System” which is based on measurements of head size (Jasper, 1958).
The mid-occipital electrode location (OZ) is on the midline.
The distance above the inion calculated as 10 % of the distance between the inion and nasion, which is 3-4 cm in most adults
Lateral occipital electrodes are a similar distance off the midline.
To have reliable VEPs, Intraoperatively, the following factors are important
Maintaining normal intraoperative physiological/hemodynamic parameters
Use of TIVA instead of inhalational anesthesia
Better stimulus delivery methods
Recording intraoperative ERG to ensure good retinal stimulation and
Employing optimal recording parameters
This presentation looks at EEG signal generation, pyramidal cells, recording of EEG, source localisation, polarity, analysis of dipole, derivations, montages,
ECochG is a variant of brainstem audio evoked response (ABR) where the recording electrode is placed as close as practical to the cochlea. We will use the abbreviation ECOG and ECochG interchangeably below. ECOG is preferable to us as it is shorter.
ECOG is intended to diagnose Meniere's disease, and particular, hydrops (swelling of the inner ear). ECOG may also be abnormal in perilymph fistula, and in superior canal dehiscence. The common feature connecting these illnesses is an imbalance in pressure between the endolymphatic and perilymphatic compartment of the inner ear.
ECOG can also be used to show that the cochlea is normal, in persons who are deaf. The cochlear microphonic of ECOG may be normal in auditory neuropathy (Santarelli and Arslan 2002) as well as other disorders in which the cochlea is preserved but the auditory nerve is damaged (Yokoyama, Nishida et al. 1999).
Finally, ECOG's have also been used to as a indicator of the temporary threshold shift that may follow noise injury (Nam et al, 2004).
MagnetoenCephaloGraphy (MEG) is a technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers.
CNIM Questions related to Mathematics and Formulas Anurag Tewari MD
There are a few questions in CNIM exam that would require you to use your knowledge of simple mathematics to derive to an answer. Here are a few representative questions. Please do read more and practice as many questions as you can.
Presented at Evolution 2013, June 24; describes an approach to teaching populations genetics at the upper undergraduate/beginning graduate level, using simulations based in R and incorporating available large genomic data sets.
Optimal Multisine Probing Signal Design for Power System Electromechanical Mo...Luigi Vanfretti
This talk presents a methodology for the design of a probing signal used for power system electromechanical mode estimation. Firstly, it is shown that probing mode estimation accuracy depends solely on the probing signal’s power spectrum and not on a specific time-domain realization. A relationship between the probing power spectrum and the accuracy of the mode estimation is used to determine a multisine probing signal by solving an optimization problem. The objective function is defined as a weighting sum of the probing signal variance and the level of the system disturbance caused by the probing. A desired level of the mode estimation accuracy is set as a constraint. The proposed methodology is demonstrated through simulations using the KTH Nordic 32 power system model.
[Research] Detection of MCI using EEG Relative Power + DNNDonghyeon Kim
* This is a summarized presentation material for the conference paper:
Donghyeon Kim, and Kiseon Kim "Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network," IEEE EMBC 2018
* It was addressed in A-GIST group, study team for Artificial Intelligence in Gwangju Institute of Science and Technology (GIST)
* Youtube (in Korean): https://youtu.be/2maphOXkB6k
Measuring EEG in vivo for Preclinical Evaluation of Sleep and Alzheimer’s Dis...InsideScientific
In this webinar, sponsored by Data Sciences International (DSI), Dr. Marco Weiergräber and Dr. Jennifer Teske discuss methodology and application of DSI telemetry in small animal models. By way of case study, each presents procedure, best-practices and shares experimental results in hopes to demonstrate the novel application of complimentary technologies for measuring neuronal activity.
Specifically, Dr. Weiergräber presents implantation process for the F20-EET and HD-XO2 transmitters, including pre-, intra- and postoperative specifics that drive successful surgery and recordings. Furthermore, he illustrates how to perform simultaneous video-EEG recordings and how to prepare for downstream analysis of spontaneous and pharmacologically-induced hippocampal theta oscillations. Dr. Weiergräber describes analysis of theta activity and presents a self-made automatic detection system for highly organized theta oscillations.
Following, Dr. Jennifer Teske presents how DSI telemetry can used to determine energy efficiency of non-REM and REM sleep, and how telemetry can be combined with metabolic systems to quantify components of energy expenditure and movement in rodents. Specifically, she discusses experimental procedure for successful telemeter implantation and integration of DSI hardware with metabolic systems. She shares data collected using DSI’s F40-EET telemetry implant while concurrently measuring sleep, physical activity, feeding and energy expenditure using a Promethion Metabolic system, and shows how to calculate energy efficiency for non-REM and REM sleep stages, as well as individual components of total energy expenditure.
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Tutorial on data science, what's it like to be a data scientist, big data, the data scientific method, probabilistic algorithms, map-reduce, sensor data analysis, visualization of twitter and foursquare feeds, open source tools (R, Python, NoSQL)
Artifact Detection and Removal from In-Vivo Neural SignalsMd Kafiul Islam
Background
In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest.
New method
The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed.
Results
Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio.
Comparison with existing methods
Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms.
Conclusion
Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.
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/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's entangled aventures in wonderlandRichard 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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
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
models for evolution of the dark matter halo mass function.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
fNIRS data analysis
1. fNIRS data analysis
• Biophysics
• Donders Center for Neuroscience
• Donders Institute for Brain, Cognition and Behavior
• Science Faculty, Radboud University
• Otorhinolaryngology
• Medical Faculty, RadboudUMC
• Donders Hearing and Implants
• Radboud Research Facilities
• Cochlear
• Advanced Bionics
Marc van Wanrooij, Luuk van de Rijt, Anja Roye, Guus van Bentum, Ad Snik, Emmanuel
Mylanus, John van Opstal
2. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
3. • Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
5. Recording fNIRS - Raw data
• Data needs to be read into Matlab workspace
Raw
20 25 30
Time (sec)
Amplitude (au)
765 nm
858 nm [OD,xmlInfo]=oxy3read_function();
% propietary Matlab file from Artinis
% quite cumbersome as we need to manually enter
% filename.
% Also, AD board signals are not extracted
6. • Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
7. Artifact rejection and correction - Cardiac oscillation
• Strong cardiac oscillation in fNIRS raw signals is undesirable for measuring evoked cortical
hemodynamic responses
out = prctile(OD(1,:),[2.5 97.5]); % outliers
sel1 = OD(1,:)out(2) OD(1,:)out(1); % selection
pa_getpower(OD(ii,sel1)-mean(OD(ii,sel1)),250,'display',1); % power spectrum
1
0.8
0.6
0.4
0.2
0
Power Spectrum
0.1 1 10
Frequency (kHz)
Amplitude (au)
8. Artifact rejection and correction - Scalp coupling
• Strong cardiac oscillation in fNIRS raw signals indicates a good contact between the optical
probe and the scalp
20 25 30
0
ï
SCI = 0.99
Time (sec)
Amplitude (au)
765 nm
858 nm
Odcardiac(ii,:)= resample(OD(ii,:),10,Fs);
% we resample the data: this is better than
% downsample because it deals with anti-aliasing,
% but there is a discussion about this
ODcardiac(ii,:)= pa_bandpass(ODcardiac(ii,:),[0.5 2.5],5);
% we band-pass between 0.5 and 2.5 Hz to keep cardiac
% component only.
r = corrcoef(ODcardiac(ii,sel),ODcardiac(ii+1,sel));
r = r(2)^2; % Scalp coupling index
Pollonini, L., Olds, C., Abaya, H., Bortfeld, H., Beauchamp, M. S., Oghalai, J. S. (2014). Auditory cortex
activation to natural speech and simulated cochlear implant speech measured with functional near-infrared
spectroscopy. Hearing Research, 309, 84–93. doi:10.1016/j.heares.2013.11.007
9. Artifact rejection and correction - Scalp coupling
Rejection 1
• Reject channels with poor scalp coupling (SCI0.75)
10. Sidenote - from optical densities to concentration changes
http://en.wikipedia.org/wiki/Near-infrared_spectroscopy
• Basically, you can do it yourself with matrix multiplication:
e = [eHbR1*d eHbO1*d; eHbR2*d eHbO2*d]; % absorption coefficients
dOD = [dOD1;dOD2]; % optical densities
dX = eM^-1*dOD; % concentration changes
• Artinis has some Matlab code available
[t,O2Hb,HHb]=single_ch(OD,xmlInfo,2,1,[3,4]);
11. Artifact rejection and correction - motion artifact
• Usually we throw away data that is contaminated by motion artifacts. Example here shows
some onset artefacts, that are selected by an automatic artifact removal.
ODz(ii,:) = zscore(OD(ii,:); % ztransform the data
% remove some outliers
out = prctile(ODz(ii,:),[2.5 97.5]);
sel = ODz(ii,:)out(2) ODz(ii,:)out(1);
OD(ii,sel) = NaN;
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12. Artifact rejection and correction – physiological/scalp noise
• For a simple fNIRS measurement, reference channel subtraction can improve data
%% Reference channel subtraction
b = regstats(chanSig,chanRef,'linear','r');
chanSig = b.r; % residuals
Main
channel Reference
channel Clear
↓
signal
-‐ =
13. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
14. Filtering
• If (physiological) noise (e.g. variations that you are not interested in) is not removed or
corrected, one typically filters the data.
15. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
16. Computing averages
• Average over events / subjects
function MU = getblock(nirs,chanSig,sensmod)
fs = nirs.fsample;
fd = nirs.fsdown;
onSample = ceil([nirs.event.sample]*fd/fs); % onset and offset of stimulus
offSample = onSample(2:2:end); % offset
onSample = onSample(1:2:end); % onset
stim = {nirs.event.stim};
selOn = strcmp(stim,sensmod);
selOff = selOn(2:2:end);
selOn = selOn(1:2:end);
Aon = onSample(selOn);
Aoff = offSample(selOff);
mx = min((Aoff - Aon)+1)+150;
nStim = numel(Aon);
MU = NaN(nStim,mx);
for stmIdx = 1:nStim
idx = Aon(stmIdx)-100:Aoff(stmIdx)+50; % extra 100 samples before and
after
idx = idx(1:mx);
MU(stmIdx,:) = chanSig(idx);
end
MU = bsxfun(@minus,MU,mean(MU(:,1:100),2)); % remove the 100th sample, to set y-origin to
stimulus onset
18. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
19. −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 0.5
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Quantification of amplitudes and latencies
• Maximum amplitude in average trace / Generalized linear model
20. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis: see Guus van Bentum
21. Analysis pipeline
• Recording fNIRS
• Artifact rejection and correction (scalp coupling, motion artifact, physiological noise)
• Filtering
• Computing average
• Quantification of amplitudes and latencies
• Statistical analysis
Note similarity to FieldTrip EEG analysis
http://fieldtrip.fcdonders.nl/tutorial/introduction
22. Analysis packages
• Statistical analysis of fNIRS Tak, S., Ye, J. C. (2014). Statistical analysis of fNIRS data: A comprehensive review.
NeuroImage, 85 Pt 1(null), 72–91. doi:10.1016/j.neuroimage.2013.06.016
• HOMER Huppert, T. J., Diamond, S. G., Franceschini, M. A., Boas, D. A. (2009). HomER: a review of time-series analysis
methods for near-infrared spectroscopy of the brain. Applied Optics, 48(10), D280–98. Retrieved from
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2761652tool=pmcentrezrendertype=abstract
• Motion artifact correction Cooper, R. J., Selb, J., Gagnon, L., Phillip, D., Schytz, H. W., Iversen, H. K., … Boas, D. A.
(2012). A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Frontiers in
Neuroscience, 6, 147. doi:10.3389/fnins.2012.00147
• NIRS Analysis package Fekete, T., Rubin, D., Carlson, J. M., Mujica-Parodi, L. R. (2011). The NIRS Analysis Package:
noise reduction and statistical inference. PloS One, 6(9), e24322. doi:10.1371/journal.pone.0024322