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Archive for the ‘Computational Biology/Systems and Bioinformatics’ Category


SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

Other related articles published in this Open Access Online Scientific Journal include the following:

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    Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics

    Nov 28, 2015 | Kindle eBook

    by Justin D. Pearlman MD ME PhD MA FACC and Stephen J. Williams PhD
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    Perspectives on Nitric Oxide in Disease Mechanisms (Biomed e-Books Book 1)

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    Milestones in Physiology: Discoveries in Medicine, Genomics and Therapeutics (Series E: Patient-Centered Medicine Book 3)

    Dec 26, 2015 | Kindle eBook

    by Larry H. Bernstein MD FACP and Aviva Lev-Ari PhD RN
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    Genomics Orientations for Personalized Medicine (Frontiers in Genomics Research Book 1)

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    Regenerative and Translational Medicine: The Therapeutic Promise for Cardiovascular Diseases

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    Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation: The Art of Scientific & Medical Curation

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Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

 

This article presents Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single molecule DNA sequencing

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

Other related articles published in this Open Access Online Scientific Journal include the following:

UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC

Curator: Tilda Barliya, PhD

Analysis of  Bhasin et al paper and Literature search

Table 1: 5-genes classifiers as biomarkers for PDAC:

Gene symbol Gene name Subcellular localization
ECT2 Epithelial cell transforming sequence 2 oncogene Nucleus, cytoplasm
AHNAK2 AHNAKE nucleoprotein 2 Plasma membrane, cytoplasm
POSTN Periostin, osteoblast specific factor Extracellular space
TMPRSS4 Transmembrane protease, serine 4 Plasma membrane

 

SERPINB5 Serpin peptidase inhibitor, clade B (ovalbumin) member 5 Extracellular space


Introduction
:

  • Bhasin et al, conducted a beautiful study using a powerful meta-analysis from different sources to identify the “important/classifier” genes associated with Pancreatic Cancer (PDAC).
  • The authors identified 5 genes that were considered as good classifiers (table 1).
  • It is important to note that the meta-analysis was performed on tissue and microdissection samples.
  • In their summary, the authors aim to validate these genes in blood/urine samples.
  • While these genes might be over expressed in tissue samples it may not be true to their existence in blood and careful examination and validation is required.
  • Liquid biopsies are emerging as the go-to use tools for disease detection, mostly aimed for early diagnosis.
  • Liquid biopsies are non-invasive biopsies of blood, urine (potentially saliva) and their “exotic” components, i.e miRNA, exosomes etc.
  • Since Liquid biopsies are non-invasive, they are painless and patients are more complied.
  • It is important to note that there is a gap between the expression of a gene or a protein in tissue section and their expression in the blood and may not necessarily correlate.
  • It will be very interesting to follow their research and future outcomes.

Additional References:

  • TMPRSS4: an emerging potential therapeutic target in cancer.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4453593/

  • The tumour trail left in blood

http://www.nature.com/nature/journal/v532/n7598/full/532269a.html

Aashir Awan, PhD, wrote on 12/28/2016

I was wondering if these same 5 genes were upregulated in the pancreatic ductal adenocarcinoma cell lines that are available out there.  In doing cell biology work, there is always a dilemma whether cancer cell lines correctly re-capitulate in vivo tumors or not.  Personally, I prefer primary cell lines to do analysis but this finding can be used to test primary vs cell line.  In addition, I’ve attached the gene network for Ect2.  If you look carefully, the two big proteins that jump out are RACGAP1 and KIF23.  I think in designing therapies, combinatorial targets can yield the best outcomes.  Drugs directed towards two or more targets would seem ideal in my opinion.

ect2

Gene Network for Ect2

Original Research
Oncotarget. 2016 Apr 26;7(17):23263-81. doi: 10.18632/oncotarget.8139.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier.

Abstract

PURPOSE:

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN:

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS:

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS:

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

KEYWORDS:

bioinformatics; biomarkers; meta-analysis; pancreatic cancer; transcriptome

PMID:
26993610
PMCID:
PMC5029625
DOI:
10.18632/oncotarget.8139

SOURCE

Oncotarget, Vol. 7, No. 17 – Referred as PDF, above

 

BIDMC Researchers Discover Early Indicators of Pancreatic Cancer

LibermannBhasin_PancreasCancerStudy

Markers may help doctors detect pancreatic cancer before it becomes deadly

In photo: First author Manoj Bhasin, PhD, and co-senior author Towia Libermann, PhD, Co-Director and Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

BOSTON – Pancreatic cancer, the fourth leading cause of cancer death in the United States, is often diagnosed at a late stage, when curative treatment is no longer possible. A team led by investigators at Beth Israel Deaconess Medical Center (BIDMC) has now identified and validated an accurate 5-gene classifier for discriminating early pancreatic cancer from non-malignant tissue. Described online in the journal Oncotarget, the finding is a promising advance in the fight against this typically fatal disease.

“Pancreatic cancer is a devastating disease with a death rate close to the incidence rate,” said co-senior author Towia Libermann, PhD, Director of the Genomics, Proteomics, Bioinformatics and Systems Biology Center at BIDMC and Associate Professor of Medicine at Harvard Medical School (HMS). “Because more than 90 percent of pancreatic cancer cases are diagnosed at the metastatic stage, when there are only limited therapeutic options, earlier diagnosis is anticipated to have a major impact on extending life expectancy for patients. There has been a lack of reliable markers, early indicators and risk factors associated with pancreatic cancer, but this new way of differentiating between healthy and malignant tissue offers hope for earlier diagnosis and treatment.”

The investigators used a number of publicly available gene expression datasets for pancreatic cancer and developed a strategy to reanalyze these datasets together, applying rigorous statistical criteria to compare different datasets from different laboratories and different platforms with each other. The team then selected a subset of data for developing a panel for differentiating between pancreatic cancer and healthy pancreas tissue and thereafter applied this “Pancreatic Cancer Predictor” to the remaining datasets for independent validation to confirm the accuracy of the markers.

After demonstrating and independently validating that a 5-gene pancreatic cancer predictor discriminated between cancerous and healthy tissue, the researchers applied the predictor to datasets that also included benign lesions of the pancreas, including pancreatitis and early stage cancer. The predictor accurately differentiated pancreatic cancer, benign pancreatic lesions, early stage pancreatic cancer and healthy tissue. The predictor achieved on average 95 percent sensitivity and 89 percent specificity in discriminating pancreatic cancer from non-tumor samples in four training sets and similar performance (94 percent sensitivity, 90 percent specificity) in five independent validation datasets.

“Using innovative data normalization and gene selection approaches, we combined the statistical power of multiple genomic studies and masked their variability and batch effects to identify robust early diagnostic biomarkers of pancreatic cancer,” said first author Manoj Bhasin, PhD, Co-Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center and Assistant Professor of Medicine at HMS.

“The identification and initial validation of a highly accurate 5-gene pancreatic cancer biomarker panel that can discriminate late and early stages of pancreatic cancer from normal pancreas and benign pancreatic lesions could facilitate early diagnosis of pancreatic cancer,” said co-senior author Roya Khosravi-Far, PhD, Associate Professor of Pathology at BIDMC. “Our findings may open a window of opportunity for earlier diagnosis and, consequently, earlier intervention and more effective treatment of this deadly cancer, leading to higher survival rates.”

The first diagnostic application of the panel may be for analyses of fine needle biopsies routinely used for diagnosing pancreatic cancer and for determining the malignant potential of mostly benign pancreatic cysts that can sometimes be precursors of pancreatic cancer. In addition to providing a new tool for diagnoses, the research may also lead to new insights into how pancreatic cancer arises.

“Because these five genes are ‘turned on’ so early in the development of pancreatic cancer, they may play roles as drivers of this disease and may be exciting targets for therapies,” said Libermann. Most of the five genes—named TMPRSS4, AHNAK2, POSTN, ECT2 and SERPINB5—have been linked to migration, invasion, adhesion, and metastasis of pancreatic or other cancers.

The scientists next plan to evaluate the precise roles of the five genes and to validate the accuracy of their diagnostic assay in a prospective clinical study. “Moving forward, we will explore the potential to convert this tissue-based diagnostic into a noninvasive blood or urine test,” Libermann said.

“To further enhance the diagnostic power of this biomarker, we plan to expand it by including non-coding RNAs, proteins, metabolites and mutations associated with pancreatic cancer. This will result in development of the first of its kind biomarker that gauges pancreatic cancer alterations from multiple genomic angles for making highly accurate diagnoses,” added Bhasin. Such an inexpensive and simple test could help transform the landscape of pancreatic cancer and help prevent many of the estimated 330,000 deaths that it causes worldwide each year.

Study coauthors include BIDMC investigators Kenneth Ndebele, Octavian Bucur, Eric Yee, Jessica Plati, Andrea Bullock, Xuesong Gu, Eduardo Castan, Peng Zhang, Robert Najarian, Maria Muraru and Rebecca Miksad, and the University of Nebraska-Lincoln’s Hasan H. Otu. The work was supported by the National Institutes of Health, National Cancer Institute and Ben and Rose Cole Charitable Pria Foundation.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

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LIVE – Day 1, OCTOBER 18 @The 16th annual EmTech MIT – A Place of Inspiration, October 18-20, 2016, Cambridge, MA

 

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

pharma_bi-background0238

will cover in REAL TIME

The 16th annual EmTech MIT – A Place of Inspiration, October 18-20, 2016, Cambridge, MA

http://events.technologyreview.com/emtech/16/

In attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN

Editor-in-Chief

http://pharmaceuticalintelligence.com

@pharma_BI

@AVIVA1950

#emtechmit

 

TUESDAY, OCTOBER 18, 2016

SOURCE

http://events.technologyreview.com/emtech/16/#section-schedule

  • 8:00
    Registration & Breakfast
  • 9:00
    Opening Remarks
    Emerging technologies, including:

    – Rethinking Energy

    — Rewrite Biology

    – Virtual Reality, Augmented Life

    – Artificial intelligence

    – Global Connectivity

    – Engineering a Healthy Planet

    – Spotlight talks on the 10 Breakthrough Technologies

    – Celebration of the 2016 Innovators Under 35

  • 9:15
    Connecting for Greater Opportunity: Defining the Digital Era – from FaceBook 1.2 Billion customers around the World.
     – awareness affordability and infrastructure

    – 4G in US, Scandenavia, Japan, 3G in Europe, India 2G network, distribution of World population, Density of 1 sq Km, 1.6 Billion do not have mobile connections. Terragraph in Urban center to leapfrog UAVs to be used for connectivity. Terragraph Overview – ETHERNET CONNECTIVITY – GIGABIT – unlicensed 60GHz Spectrum, cloud computing, Open/R: IPv6 – Laser communication: comparable to fiber optics, 10s of Gbps per second, atmosphere absorbs and scatter: pointing and tracking system, Satellite. FACEBOOK Priorities: Density of Population, Gov’t will be favorable, population have not enough — #1 is INDIA, CHINA

 

  • 9:45

    Innovators Under 35 Introduction – Brian Bergstein, Editor of Technology Review

  • 10:00

    Meet Young Inventors

     – Blendoor – Stephanie Lampkin
    – MACH – Eshan Hoque Social Skills Trainer – My Automated Conversation Coach – ROCspeak.com
    – HandTalk – Ronaldo Tenorio – Deaf People 6 million people in Brazil, barrier between Hearing and Deaf people – sign language
    – Energy Systems Requires Water, Lake Mead by Kelly Sanders – energy vs bottle water
  • 10:30
    Break & Networking
  • 11:00 Antolio Regalado, Editor Biotech at MIT Technology Review

    Gene Therapy- A New Era of Medicine

    CEO & Founder, Intellia Therapeutics, Nessan Bermingham, PhD, 2018 there will be the product in Clinics
    – Gene Editing,
    – CRISPR Cas9 – immune system of Bacteria, using the mechanism in Bacteria for Human cells, Cellular level
    – cut DNA, coopting and shut down  – cut a piece of DNA convert a mutation,
    – injection of cRISPR protein – correction and the existing malformed is repaired no longer expressed
    – stem cells modifies and re-injected
    – KO: ATTR, AATD, HBV
    – Repair: AATD, HSCs
    – CAR-T cells
    – genetic engineering vs gene repair
    – equal access to therapy

    MGH – Cellular Immunotherapy Program – Marcella Maus, PhD on T-Cell research

    – blocking the checkpoints by antibo- dies, Use Tcells as drugs, scale the process, manufacturing process, recover T cell from Blood, from biopsy, who is responding and who does not respond? – Leukemia, CAR- T cells, multiple Myeloma, CURE is early to use but now it can be used COMBINATION of gen therapy follwoing gene editing and immunotherapy, CAR T- cell products for leukemia and lymphoma
  • Katheirne High, Spark Therapeutics

  • bring therapeutics to people – research to clinic
  • DNA defective, engineered from AAV: Vector, DNA, Target tissue to delivery
  • conjenetive blindness – investigational trial – get vector to retina by surgeon – clinical gain of function notices in 30 days group injected vs control group – impproved light sensitivity and mobility
  • first gene therapy for blindness
  • Himophilia – vector injected in the Liver where blood factors are produced
  • models of therapeuitcs: Bone marrow transplantation,
  • clinical cell therapy – low efficacy in adverse events – academic medical center and NIH interested vs Biotech
  • as Clinical Trials were successful Biotech and Pharma got interested
  • Access, pricing, reimbursement – How gene therapy is ONCE in a life time not an an infusion on a reccurent basis over many years – Hymophillia,  no pharmacological treatment to blindness
  • duration of expression 5 years and counting in UK, follwoing gene therapy
  • premature for cure

 

  • 2:00

    The Robots Among Us –

    Stephanie Tellex, Brown University
    – 35 cm lens – Robots Distributive Lab @MIT – movie
    Sangbae Kim – Robots at Work – Robotics Mobility of the Future @MIT Mechanical Engineering
    – Physical interaction – BMW i3 Factory – automotive production
    – Robot design paradigms: Manufacturing (lack of compliance) vs construction (lack of efficiency)
    – Robot design for mobility – a robot that runs – MIT Cheeta Model
    MIT  meche Biomimetic Robotics Lab – high torque, high impact mitigation
    – Hermes project:
    Karl Iagnemma – Intellignet Machines – nuTonomy – Singapore  – Autonomous machines deliver parcels and car is never distracted – driverless car
    – Robotics Lab at MIT – smart car, self driven cars, ability to learn from experience – 2017 – double fleet of drivers
  • 12:30

    Lunch & Networking

    Meet Young Inventors under 35

  • Jagdish Chaturvedi – ENTraview – device was licensed to Medtronic and to an Indian company, developed two more product InnAccel – Bangalor — product design company
  • Wei Gao – Wearable Tech – Wearable Human Sweat Sensors
  • Imaging Technologies by Muyinatu BiSI Bell Sounds – Amplitude vs Coherence
  • Heather Bowerman – Hormonal Disease: Endometriosis 10% of Woman – microRNA DotLab
  • 12:00

  • 3:00 Meet the 2016 Innovators Under 35

Jiawei Gu Ling Robotics – Intelligent life with Robots and AI inside – Life UnpluggedInfinite of robots, Intelligence of things,
Nora Ayanian, USC – Computer vision
Maithilee Kunda, Vanderbuilt University, CS – Computer vision
Oriol V. Google, AI & Machine Learning – Deep Natural Network
Machine Translation, Text to Speech,
3:30
  1. Break & Networking

4:00

A.I.’s Next Leap Forward – David Cox at Harvard University

  1. networks, computations and computers as metaphor for Neuro Science and Brain Science
  2. Brains are computational systems – Petaflops of computations
  3. Deep learning, Artificial neuronet works – NeuroNets
  4. Vision theory – ImageNet error rate in decrease tendency
  5. Machine Intelligence from Cortical – DARPA
  6. Genetics and microscopes – like wire tap in the brain
  7. Model of Brain mapped – set hypothesis on function and anatomy

 Big Data – Ruslan Salakhutdinov. Carnegie Mellon

  1. Natural language
  2. multimodal learning – nearest Images
  3. unsupervised learning – no labelled data, natural Story Telling
  4. Image understanding – deep learning
  5. Caption generation
  6. Semantic Relatedness: Recurrent Neural Network
  7. One shot learning
  8. Transfer learning
  9. Summary: Image tagging, Category hierarchy, Speech recognition

XPrize

  • Google Lunar
  • Qualcomm Tricorder
  • Casio Cardon
  • Shell Ocean Discovery

5:00

Lemelson-MIT Prize Honors & Reception

Ramesh Raskar, Associate Professor, MIT Media Lab Camera Culture Group

Making Invisible Visible: Matter

  1. Light to slow motion
  2. Multi-path analysis
  3. Published in Nature 2012
  4. DARPA REVEAL Program 2015
  5. Optical Brush Endoscope
  6. Optical matter – reading in spectrum THz Imaging
  7. wifi Camera – see through walls
  8. EyeNetra: Eyeglasses Perscription on Phone
  9. EyeSelfie
  10. Camera for the Visual Challenged
  11. Peer-to-Peer Invention – No upfront Team, Problem – Solution – REDX – sleep apnea
  12. Blood supply Chain
  13. Monitize garbage
  14. Informal Sector: Street address for all – $1 wearable
  15. The World is a Lab – REDX.io – Affordable Excellence

Lemelson Family FOundation based in Oregon is recognizing Ramesg Raskar for 2016 – MIT Prize. Inventors are recognized in the last 20 years – translation of ideas to products.

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