Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
https://www.youtube.com/watch?v=Y_-o-4rKxUk
Machine learning powered metabolomic network analysis
Dmitry Grapov PhD,
Director of Data Science and Bioinformatics,
CDS- Creative Data Solutions
www.createdatasol.com
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
https://www.youtube.com/watch?v=Y_-o-4rKxUk
Machine learning powered metabolomic network analysis
Dmitry Grapov PhD,
Director of Data Science and Bioinformatics,
CDS- Creative Data Solutions
www.createdatasol.com
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Advanced strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs.
Case Study: Overview of Metabolomic Data Normalization StrategiesDmitry Grapov
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Automation of (Biological) Data Analysis and Report GenerationDmitry Grapov
I've been experimenting with automating simple and complex data analysis and report generation tasks for biological data and mostly using R and LATEX. You can see some of my progress and challenges encountered.
Harnessing The Proteome With Proteo Iq Quantitative Proteomics Softwarejatwood3
Learn how successful researchers are using ProteoIQ to streamline their proteomic data analysis.
Centralize data analysis on a single software platform
Most laboratories have multiple MS platforms with different software packages. ProteoIQ simplifies data analysis as a vendor independent software platform supporting qualitative and quantitative analysis.
Learn how to achieve robust peptide and protein quantification
ProteoIQ is the only commercial software platform supporting all popular forms of quantification. Learn how ProteoIQ performs protein and peptide quantification using isobaric tags, isotopic labels and label free methods including intensity based peptide profiling.
Elucidate biological significance
Learn how to integrate biological databases with ProteoIQ. Quickly move from MS results to the discovery of novel biological insights through an integrated biological annotation pipeline.
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Advanced strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs.
Case Study: Overview of Metabolomic Data Normalization StrategiesDmitry Grapov
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Automation of (Biological) Data Analysis and Report GenerationDmitry Grapov
I've been experimenting with automating simple and complex data analysis and report generation tasks for biological data and mostly using R and LATEX. You can see some of my progress and challenges encountered.
Harnessing The Proteome With Proteo Iq Quantitative Proteomics Softwarejatwood3
Learn how successful researchers are using ProteoIQ to streamline their proteomic data analysis.
Centralize data analysis on a single software platform
Most laboratories have multiple MS platforms with different software packages. ProteoIQ simplifies data analysis as a vendor independent software platform supporting qualitative and quantitative analysis.
Learn how to achieve robust peptide and protein quantification
ProteoIQ is the only commercial software platform supporting all popular forms of quantification. Learn how ProteoIQ performs protein and peptide quantification using isobaric tags, isotopic labels and label free methods including intensity based peptide profiling.
Elucidate biological significance
Learn how to integrate biological databases with ProteoIQ. Quickly move from MS results to the discovery of novel biological insights through an integrated biological annotation pipeline.
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Combining R With Java For Data Analysis (Devoxx UK 2015 Session)Ryan Cuprak
Java is a general-purpose language and is not particularly well suited for performing statistical analysis. Special languages and software environments have been created by and for statisticians to use. Statisticians think about programming and data analysis much different from Java programmers. These languages and tools make it easy to perform very sophisticated analyses on large data sets easily. Tools, such as R and SAS, contain a large toolbox of statistical tools that are well tested, documented and validated. For data analysis you want to use these tools.
In this session we will provide an overview of how to leverage the power of R from Java. R is the leading open source statistical package/language/environment. The first part of the presentation will provide an overview of R focusing on the differences between R and Java at the language level. We’ll also look at some of the basic and more advanced tests to illustrate the power of R. The second half of the presentation will cover how to integrate R and Java using rJava. We’ll look at leverage R from the new Java EE Batching (JSR 352) to provide robust statistical analysis for enterprise applications.
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
dkNET Webinar: Multi-Omics Data Integration for Phenotype Prediction of Type-...dkNET
Abstract
Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.
Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida
Upcoming webinars schedule: https://dknet.org/about/webinar
Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral...ijtsrd
This paper presents the development of a hybrid dynamic expert system for the diagnosis of peripheral diabetes and remedies using a rule based machine learning technique. The aim was to develop a solution to the risk factors of peripheral diabetes. The methodology applied in this study is the experimental method, and the software design methodology used was the agile methodology. Data was collected from Nnamdi Azikiwe University Teaching Hospitals NAUTH and the Lagos State University Teaching Hospital LASUTH for patients between the ages of 28 87years suffering from peripheral neuropathy. Other methods used were data integration by applying uniform data access UDA technique, data processing using Infinite Impulse Response Filter IIRF , data extraction with a computerized approach, machine learning algorithm with Dynamic Feed Forward Neural Network DFNN , rule base algorithm. The modeling of the hybrid dynamic expert system and remedies was achieved using the DFNN for the detection of DPN and a rule based model for remedies and recommendations. The models were implemented with MATLAB and Java programming languages. The result when evaluated achieved a Mean Square Error MSE of 4.9392e 11 and Regression R of 0.99823. The implication of the result showed that the peripheral diabetes detection model correctly learns the peripheral diabetes attributes and was also able to correctly detect peripheral diabetes in patients. The model when compared with other sophisticated models also showed that it achieved a better regression score. The reason was due to the appropriate steps used in the data preparation such as integration and the use of IIFR filter, feature extraction, and the deep configuration of the regression model. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A real life example to show the added value of the Phenotype Database (dbNP)....Chris Evelo
NuGO has initiated the development of the Phenotype Database (dbNP). This database is developed together with several other consortia (e.g. Netherlands Metabolomics Centre) and is currently used within several European projects, such as Food4me, NU-AGE, Bioclaims and Nutritech.
The Phenotype Database (www.dbnp.org) is a web-based application/database that can store any biological study. We used this application to perform an analysis on a combination of several studies with the objective to test if it is possible to answer new research questions using a ‘virtual cohort’.
Study comparison:
The assessment of the health status of an individual is an important but challenging issue. Nowadays, challenge tests are proposed as a method to assess and quantify health status. We would like to find mechanistic explanations for differences in clinical subgroups and to develop a metabolomics platform based fingerprint at baseline that represents important parameters of the challenge test. Currently, there is not one single study available that includes enough subjects from specific clinical subgroups to develop such a fingerprint or study the biological processes specific for those subgroups. Therefore, we developed a toolbox that facilitates the combined analysis of multiples studies.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEMEPublication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEME Publication
Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which include: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which includes: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which includes: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
Describes PROs with examples of why and how to use them. Details FDA draft guidance for selection, development and validation of PROs. Features the levels of evidence of ePRO migration.
Similar to Data analysis workflows part 1 2015 (20)
Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/
The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for:
* Preparing raw data for analysis
* Multivariate data exploration
* Supervised clustering
* Machine learning – classification model validation and feature selection
* Network analysis - biochemical, structural similarity and correlation networks
* Network mapping – putting it all together to create a publication quality network
url:
https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integratio...Dmitry Grapov
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
I am always looking for the next data science, machine learning and visualization challenge.
Here is a link to my up to date
resume:
https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
cv:
https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
7. Projection of Data
The algorithm defines the position of the light source
Principal Components Analysis (PCA)
• unsupervised
• maximize variance (X)
Partial Least Squares Projection to
Latent Structures (PLS)
• supervised
• maximize covariance (Y ~ X)
James X. Li, 2009, VisuMap Tech.
PC1
PC2