Presenter: Angela Oliveira Pisco , PhD
Abstract
Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726
Resource link: https://tabula-sapiens-portal.ds.czbiohub.org
Upcoming webinars schedule: https://dknet.org/about/webinar
For more course tutorials visit
www.newtonhelp.com
• how forensic scientists take advantage of genomic variations in noncoding regions of DNA
• the techniques of polymerase chain reaction (PCR) and gel electrophoresis
Species delimitation - species limits and character evolutionRutger Vos
Lecture slides for the program orientation Evolutionary Biology at the Institute of Biology Leiden, the Netherlands. Thursday, September 7th, 2017.
Lecture notes are here: https://docs.google.com/document/d/e/2PACX-1vRIv5mKK1fjBby--u97emC7hrqXUbxFQZe63P1FpguuhHLG6xykbwXKeKXCUE5W-LSpakXYCI621xCK/pub
Presenter: Angela Oliveira Pisco , PhD
Abstract
Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference...Full abstract: https://dknet.org/about/blog/2726
Resource link: https://tabula-sapiens-portal.ds.czbiohub.org
Upcoming webinars schedule: https://dknet.org/about/webinar
For more course tutorials visit
www.newtonhelp.com
• how forensic scientists take advantage of genomic variations in noncoding regions of DNA
• the techniques of polymerase chain reaction (PCR) and gel electrophoresis
Species delimitation - species limits and character evolutionRutger Vos
Lecture slides for the program orientation Evolutionary Biology at the Institute of Biology Leiden, the Netherlands. Thursday, September 7th, 2017.
Lecture notes are here: https://docs.google.com/document/d/e/2PACX-1vRIv5mKK1fjBby--u97emC7hrqXUbxFQZe63P1FpguuhHLG6xykbwXKeKXCUE5W-LSpakXYCI621xCK/pub
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
13. About Me - Max Tolkoff
● PhD from UCLA in Biostatistics
● Tree based phylogenetic algorithms for studying phenotypes
● Bayesian methods and model selection