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Archive for the ‘LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management’ Category


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2.0 LPBI is a Very Unique Organization

Author: Aviva Lev-Ari, PhD, RN

 

Leaders in Pharmaceutical Business Intelligence aka LPBI is a Registered Domain Name on WordPress.com.

  • LPBI is a Cloud-based Internet Business.

LPBI s not a manufacturing plant or a brick and mortar chain with Assets and Liabilities and AMORTIZATION and the discount rate that is chosen for the present value calculation (NPV) – these are terms not used for Intellectual Property portfolios and not used for MEDIA ventures.

Our Content is updated daily, it growth daily. We enjoy +20,000 e-Readers a month and the planned Medical Text Analysis with machine learning (Natural Language Processing) will general ~10 new digital artifacts per each existing article [N = +6,000].

Mission

  • Addressing Information Market needs for Text Analysis with NLP in Medicine, Life Sciences and Health Care
  • Development and application of a Content monetization system of the cutting edge Blockchain IT platform of a Transactions Network customized to implement NLP an several API layers of the Blockchain architecture as featured in https://pharmaceuticalintelligence.com/blockchain-transactions-network/

Venture Type

  • LPBI is an Internet-based Media e-Scientific Publisher and Medical Text Analysis with Machine Learning and Content Monetization of existing digital products and of to be produced digital products by Natural Language Processing (NLP)
  • NLP and Blockchain are cutting edge technologies [full flag implementation Blockchain is 5 years old!!].
  • When these two are combined together as LPBI 2.0 strategy states – a technological frontier for content monetization is born and could be applied to any content, not only pharmaceutical & medical, the LPBI pioneering case.
  • NOVELTY in methodologies, practice and digital artifacts in 3.3 GIGA BYTES.

2012-2020 – Milestones accomplished by 1.0 LPBI

  1. Multi-authoring system not in use by any other website on WordPress.com – World LARGEST hosting company of independent websites. It was Dr. Lev-Ari’s idea to augment the functionality and usage of the infrastructure from single to a multi-author system.
  2. IP asset Class I: 2MM e-Readers are recorded for the content on the Journal, +6,000 articles and 730 categories of research. The Curation Methodology was applied 6,000 times in two versions: Original Curations and Scientific Reporting.
  3. IP asset class V: Gallery of Biological Images embedded in original text producing prior art images, a Gallery of 5,100 in the WordPress.com cloud
  4. IP asset class IV: Cloud-based Platforms and Portals. Composition of Methods: Formats for Curations, Formats for electronic Table Of Contents (eTOCs), Role definition for Editors and Experts, Authors, Writers. Template system for e-Proceedings, Template system for Tweeting in real time at VOLUME [+200 tweets in 20 hours]
  5. IP asset class X: Luminary Podcasts with Life Sciences Scholars
  6. IP asset class II: BioMed e-Series of 18 Volumes in Medicine and Life Sciences featuring +50,000 pages of thematic selections and expert interpretations FRONTIERS of the scientific inquiry in five specialties in Medicine and in several disciplines in the Life Sciences, Pharmaceutics and Health Care. LPBI’s e-Books pioneered the entrance in e-Scientific Publishing. In 6/2013 when we published the 1st book, Elsevier and John Wiley – DID NOT YET HAVE ANY e-BOOKS. Page downloads from books is 135,000. All Books on Amazon.com https://lnkd.in/ekWGNqA
  7. IP asset class VII: Amazon.com transfers every 90 days Royalties on book sales and Page downloads. The Pay per View option deters and has deleterious effects on selling books. Payments for page download deposited for authors are pennies per page, though, 135,000 pages were downloaded to date and a significant sum was collected by Amazon.com.
  8. IP asset class III: We have 70 e-Proceedings documents [60 by Aviva and 10 by Dr. Williams] and 36 Tweet collections [by Aviva]. 100 Documents representing e-Proceedings of 70 Top Conferences and 36 Tweet collections of the latest 36 Conference covered, 2014-2021
  9. IP asset class IX: Intangible assets
  10. IP asset class VIII: 2,500 subscribers to the website
  11. IP asset class VI: The Team of Experts in the Organization Chart, below 

LPBI’s corporate structure is presented in the Organization Chart, below

Dr. Lev-Ari is the Founder of 1.0 LPBI, 2012-2020 and 2.0 LPBI, 2021-2025

Aviva Lev-Ari, PhD, RN is the

  • Inventor of the curation methodology for clinical interpretation of scientific findings
  • Inventor of eTOCs format for the BioMed e-Series. As Editor-in-chief, architected the five e-Series: A,B,C,D,E and specified the book Titles. Jointly, with Dr. Williams all the cover pages were designed for the 18 volumes.
  • Inventor of One click e-Proceedings generation and the Tweeting template system
  • Inventor of the fusion of NLP and Blockchain
  • Of 6,068, under her name are 3,516 articles [Dashboard read on 3/1/2021]

Twelve Economic Segments for LPBI Group’s IP – Prospects for Transfer of Ownership

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

  1.     Holding Companies, Investment Bankers and Private Equity
  2.     Information Technology Companies – Health Care
  3.     Scientific Publishers
  4.     Big Pharma
  5.     Internet Health Care Media & Digital Health
  6.     Online Education
  7.     Health Insurance Companies & HMOs
  8.     Medical and Pharma Associations
  9.     Medical Education
  10.     Information Syndicators
  11.     Global Biotech & Pharmaceutical Conference Organizer
  12.     CRO & CRA

LPBI is Open Access and Equity Sharing

  • The Open Access status will change when we will migrate all the four classes of content relevant for Text Analysis with NLP to the Blockchain Transactions Network, IT platform is currently under design.
  • Interested parties posing queries to the system (B2C) System responds by triggering a
  • Recommendation Engine response that will fetch: top articles by views in same research category, biological images in these articles, NLP results in visual graphics: the selected Article in the context of all articles in same chapter in the book it was feature. If article not in a book than in the context of other articles in same categories of research, ones selected by the recommendation engine and ranked by views.
  • Major Data Science procedures in the back-end and Domain Knowledge Expert interpretation of the graphics available for selection in several Foreign languages presented in the front-end.
  • System invokes Blockchain features embedded per LPBI specifications into the API layers of the Blockchain Transactions Network, i.e.,
  • Permissions
  • Immutable LEDGER
  • Smart Contracts
  • Cyber security of IP assets in blocks

Full feature operations of the Blockchain platform will enable

  • B2C – Digital Store on a Healthcare Digital Marketplace
  • B2B – Installation at Big Pharma and Healthcare Insurers and CRO

Financial Projections are in an Excel file with three spreadsheets:

  • 0 LPBI, 2012-2020 – AVAILABLE FOR MONETIZATION FOLLOWING INDEXING AND MIGRATION TO THE BLOCKCHAIN as soon as the platform will be up and running
  • 0 LPBI, 2021-2025 – NLP Proof-of-Concept in Phase 2 with Linguamatics and Wolfram algorithms applied on same content
  • Combined, 2012-2025

Potential opportunities for ownership transfer needs to consider the Portfolio of TEN IP Asset Classes, SIX strategies for 2021-2025 and the TEAM

Inclusive Plan

  • A Portfolio of IP, 1.0 LPBI 
  • The 2.0 LPBI Vision [NLP Proof-of-Concept to be followed by scaling up to all content of four IP asset classes & Blockchain Transactions Network for B2B & B2C. Status: design phase and
  • The Team of Experts

Under development

  • IP Valuation Model per IP asset class is needed to be compared with Master_Financials
  • Pricing Model is needed for digital products to be generated by the process of Text Analysis with NLP
  • We believe that we need a non-institution acquisition process, namely, we need to “Go Direct”

Huffington Post sold to AOL for $319MM while ZERO revenues on the books and NO INVOLVEMENT of IB/VC/PE. AOL CEO was the decision maker.

Go Direct – find an interested CEO

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February 1, 2021 – We Celebrate 1,924,400 e-Readers, 6,000 Scientific Journal Articles, 7,525 Scientific Comments, A Journal Ontology of 728 Medical & Life Sciences Research Categories, 10,440 Tags, Top Article 17,300 Views, Top Author 487,500 Views on PharmaceuticalIntelligence.com

Reporter: Aviva Lev-Ari, PhD, RN

 

1,924,462 views

7,525 comments

Content

6,001 Posts

728 Categories

10,441 Tags

Top Posts for all days ending 2021-02-01 (Summarized)

Top Authors for all days ending 2021-02-01 (Summarized)

All Time

Author Views
2012pharmaceutical

Aviva Lev-Ari, PhD, RN

487,488
larryhbern 352,642
sjwilliamspa 68,263
tildabarliya 67,030
Dror Nir 37,189
Dr. Sudipta Saha 36,676
Demet Sag, Ph.D., CRA, GCP 19,338
ritusaxena 17,448
Gail S Thornton 15,949
Irina Robu 9,513

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One Possible Strategy for an Acquisition is via a Special Purpose Acquisition Company (SPAC)

Authors: Joel Shertok, PhD, Board Member and Aviva Lev-Ari, PhD, RN, Founder, LPBI Group, 1.0 & 2.0

 

UPDATED ON 3/18/2021

That seems to be confirmed by a recent study by Michael Klausner and Emily Ruan of Stanford University and Michael Ohlrogge of New York University. The authors look at blank-cheque firms that made acquisitions between January 2019 and June 2020. They find that, in 25% of cases, the sponsor’s payout exceeded 12% of post-merger equity, compared with a median stake of 7.7%.

They also conclude that some spacs deliver far worse returns for investors than others: companies that went public through the spac route fell in value by an average of 3% after three months, 12% after six months and by a third after 12 months. They lagged behind the wider market and even further behind an index of firms that listed via ipo.

However, about half the sample is made up of “high-quality” spacs, defined as those run by former Fortune 500 bosses or set up by large private-equity firms. These perform much better, outperforming ipos and the wider market over six months (though not over 12).

SOURCES

https://www.economist.com/finance-and-economics/2021/02/16/why-spacs-are-wall-streets-latest-craze

The SPAC Bubble May Burst—and Not a Day Too Soon

A Sober Look at SPACs

57 Pages Posted: 16 Nov 2020 Last revised: 6 Mar 2021

Michael Klausner

Stanford Law School; European Corporate Governance Institute (ECGI)

Michael Ohlrogge

New York University School of Law

Emily Ruan

affiliation not provided to SSRN

Date Written: October 28, 2020

Abstract

A Special Purpose Acquisition Company (“SPAC”) is a publicly listed firm with a two-year lifespan during which it is expected to find a private company with which to merge and thereby bring public. SPACs have been touted as a cheaper way to go public than an IPO. This paper analyzes the structure of SPACs and the costs built into their structure. We find that costs built into the SPAC structure are subtle, opaque, and far higher than has been previously recognized. Although SPACs raise $10 per share from investors in their IPOs, by the time the median SPAC merges with a target, it holds just $6.67 in cash for each outstanding share. We find, first, that for a large majority of SPACs, post-merger share prices fall, and second, that these price drops are highly correlated with the extent of dilution, or cash shortfall, in a SPAC. This implies that SPAC investors are bearing the cost of the dilution built into the SPAC structure, and in effect subsidizing the companies they bring public. We question whether this is a sustainable situation. We nonetheless propose regulatory measures that would eliminate preferences SPACs enjoy and make them more transparent, and we suggest alternative means by which companies can go public that retain the benefits of SPACs without the costs.

 

Keywords: SPAC, Securities Law

Klausner, Michael D. and Ohlrogge, Michael and Ruan, Emily, A Sober Look at SPACs (October 28, 2020). Yale Journal on Regulation, Forthcoming, Stanford Law and Economics Olin Working Paper No. 559, NYU Law and Economics Research Paper No. 20-48, Available at SSRN: https://ssrn.com/abstract=3720919 or http://dx.doi.org/10.2139/ssrn.3720919

 

UPDATED on 3/17/2021

Paul R. Milgrom

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2020

Born: 20 April 1948, Detroit, MI, USA

Affiliation at the time of the award: Stanford University, Stanford, CA, USA

Prize motivation: “for improvements to auction theory and inventions of new auction formats.”

Prize share: 1/2

To cite this section
MLA style: Paul R. Milgrom – Facts – 2020. NobelPrize.org. Nobel Media AB 2021. Thu. 18 Mar 2021. <https://www.nobelprize.org/prizes/economics/2020/milgrom/facts/>

LECTURE by Paul R. Milgrom

https://www.nobelprize.org/prizes/economic-sciences/2020/milgrom/lecture/

To cite this section
MLA style: Robert B. Wilson – Interview. NobelPrize.org. Nobel Media AB 2021. Wed. 17 Mar 2021. <https://www.nobelprize.org/prizes/economic-sciences/2020/wilson/interview/>

Robert B. Wilson

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2020

Born: 16 May 1937, Geneva, NE, USA

Affiliation at the time of the award: Stanford University, Stanford, CA, USA

Prize motivation: “for improvements to auction theory and inventions of new auction formats.”

Prize share: 1/2

To cite this section
MLA style: Robert B. Wilson – Facts – 2020. NobelPrize.org. Nobel Media AB 2021. Thu. 18 Mar 2021. <https://www.nobelprize.org/prizes/economics/2020/wilson/facts/>

 

UPDATED on 2/5/2021

SPAC growth has multiplied faster than the COVID-19 virus contagion.  In fact, the pandemic frightened many companies who were contemplating an IPO in 2020.  A SPAC is a safer way to get to an IPO during market and economic hardships.  Think of it as a merger behind closed doors rather than under the magnifying glass of the SEC, investors, creditors, etc.  70% of all money raised through public offerings in January 2021 were SPACs. Per the graph, 2020 saw $82B poured into SPACs.  The single month of January 2021 saw more than 96 SPACs raise $20B.  The US is now seeing 5 new SPACs a day!

“Many of the 287 SPACs currently hunting for targets are looking for deals in hot sectors such as technology or electric vehicles, according to figures from data provider SPAC Research. Last week, six new SPACs launched: Queen’s Gambit Growth Capital —a company led entirely by women that shares a name with an opening sequence in chess and a popular show on Netflix —Legato Merger, Gores Metropoulos II, Oyster Enterprises Acquisition, TZP Strategies Acquisition and FoxWayne Enterprises Acquisition. Eight more went public on Friday”. Amirth Ramkuma / Wall Street Journal

UPDATED on 2/2/2021

Blank Check Companies’ Hunt For Innovative Medtech Start-Ups To Heat Up Further, Experts Predict

Healthcare SPAC Mergers – Overview

Chart

Source: Chardan, FactSet, CapIQ, latest close as of 01/25/2021

Joseph Hernandez, founder and CEO of Blue Water Acquisition Corp., a SPAC that is currently looking for an acquisition target in the health care space, also expects a lot more deal flow in medtech, particularly in artificial intelligence, telemedicine, cardiology, joint replacements, diagnostics and genomics.

JOSEPH HERNANDEZ, FOUNDER AND CEO, Blue Water Acquisition Corp.

“Medtechs are often undervalued in the market, but I think if you look at their risk profile, they are usually less risky than biotech companies because you usually have a product,” Hernandez told Medtech Insight.

Blue Water Acquisition announced on 15 December it raised $50m by offering 5 million units at $10. Each unit consists of one share of common stock and one warrant exercisable at $11.50. The underwriter, Maxime Group, exercised its over-allotment option, resulting in $57.5m raised.

“We have been a free-trading company for a little bit over 30 days and I can tell you we’ve seen a large number of deals – in excess of 40 deals we’ve looked at [at] this point – and continue to get deal flow here on a daily basis,” Hernandez said. Though he feels that the smaller size of the SPAC, which puts it at an acquisition target range of roughly $75-$240m, puts it in the “sweet spot.”

“There’s not a lot of players in this acquisition spectrum,” he said. The majority of deals have been on the larger side, where there are more SPACs competing for the same private companies.

Karim Anani, EY Americas Transactions Accounting Advisory Leader and EY Capital Markets Partner advising companies on strategic transactions, also expects the SPACs craze to continue ramping up in 2021.

“We’re also starting to see increased deal sizes, so the valuations and the size of the companies that SPACs are looking to merge with in the medtech space are increasing as we progress through 2020 and go into 2021,” Anani told Medtech Insight. “The amount of capital, if you consider general SPACs and what is identified for this industry, is probably $10-$14bn – it just depends how you want to cut the data.”

SPACs Bubble?

Goldman Sachs Group Inc. CEO David Solomon, among others, have expressed concerns about the sustainability of SPACs. Solomon warned in the company’s call in January that the flurry of SPACs is not sustainable in the medium-term. Goldman Sachs is among the banks benefitting from the boom.

Grossman also foresees that there will be “slowing of the pace,” noting that there are about 250 SPACs out there.

“Once it gets to 300 or 400, it starts to get crowded in each of the different spaces and there’s only a limited number of opportunities for public-ready companies,” he said. Nevertheless, he believes that 2021 will be an interesting year.

“I think you’re going to see a lot of innovative companies go public, especially in medtech.”

SOURCE

https://medtech.pharmaintelligence.informa.com/MT143415/Blank-Check-Companies-Hunt-For-Innovative-Medtech-StartUps-To-Heat-Up-Further-Experts-Predict?vid=Pharma&processId=bbaaf314-e3b9-4ed1-8631-b85f7773183b

 

1/20/2021

Overview of Leaders in Pharmaceutical Business Intelligence (LPBI)

ACQUISITION STRATEGIES AND INVESTMENT NEEDS

 

LPBI was founded by Dr Aviva Lev-Ari, RN, PhD in 2012 and developed since then to Present.

During the period 2012-2020 – LPBI developed a vast Intellectual Property portfolio of several IP Assets (LPBI 1.0):

  • 2MM e-Readers,
  • 6000 Journal articles,
  • 18 e-Books in Medicine,
  • 100 e-Proceedings & Tweet Collections and
  • 5000 Biological images

Starting in 2021, LPBI will evolve into a new entity building on LPBI 1.0 IP Portfolio, designated LPBI 2.0:

See below links for more information.

LPBI 2.0 is pursuing:

  1. Medical Text Analysis NLP, ML-AI on our content created 2012-2020
  2. Content Monetization on Blockchain: existing digital products, and ML products

LPBI is seeking potential acquirers in both Israel and the United States, as LPBI 2.0 requires both an acquirer and investor/investment to realize its strategies. This will involve the transfer of ownership of an IP Portfolio consisting of over 3.3 Gigabytes of cloud-based English text and associated biological images.

The projected acquisition will also entail new management of the IP Portfolio consisting of both 1 and 2 strategies, above, along with a team of ten Experts in Medicine and Life Sciences.

We feel that realizing these activities will require either:

  • An existing company able to execute an M&A strategy, or
  • An investor/acquirer to convert LPBI to a new ownership, assuming control of the IP and the LPBI Team.

Investment will be necessary to support these strategies:

A. The conversion of our content via Natural Language Processing (NLP) and Machine Learning (ML) /Artificial Intelligence (AI) into the corresponding graphics along with its interpretation by experts.

  • Scaling up to all contents of LPBI 1.0 IP Portfolio: Journal articles, books, e-Proceedings, Biological Images

B. The implementation of the Blockchain Transaction Network, necessary for secure monetization of LPBI existing content:

  • Journal articles, books, e-Proceedings, Biological Images
  • New products generated by NLP (hyper-graphs and expert interpretation),
  • B2C – a Digital Store in a Healthcare Digital Marketplace, and
  • B2B – installations in Big Pharma and Healthcare Insurers involve implementation of the customized design of a Blockchain transaction IT infrastructure:

These include IT design, which represents the first ever combined IT infrastructure for NLP and Blockchain transaction network:

  • Recommendation Engine,
  • Smart Contracts,
  • Permissions,
  • Immutable ledger

C. Addition operations requiring investment:

  • Original data migration from an Authoring cloud to a Transaction cloud.
  • Hosting a Digital Store in an existing Healthcare Digital Marketplace for B2C
  • B2B special installations and development of consultancy services
  • Content promotion campaigns

One possible strategy for an acquisition is via a Special Purpose Acquisition Company (SPAC)

  • Discussion of this strategy: Date TBD

 

Additional Background Sources:

LPBI 1.0:

https://pharmaceuticalintelligence.com/2019-vista/

LPBI 2.0:

https://pharmaceuticalintelligence.com/vision/

Projections

https://pharmaceuticalintelligence.com/vision/pharmaceuticalintelligence-com-journal-projecting-the-annual-rate-of-article-views/

Portfolio of 10 Intellectual Property Asset Classes

https://pharmaceuticalintelligence.com/2019-vista/

Six Strategies, 2021-2025

https://pharmaceuticalintelligence.com/vision/

The Opportunities Map

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

The Team

https://pharmaceuticalintelligence.com/knowledge-portals-system-kps/

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Potential interest in LPBI Group’s BioMed e-Series 

Advisor: Conxa Catot · BDM, ex-Elsevier

 

Conxa Catot · BDM
CONTENT ED NET
conxa.catot@contentednet.com
+34 639 357 643
Avda. Josep Tarradellas, 8, 4-4

08029 Barcelona, Spain

 

LPBI Group’s BioMed e-Series – 18 Volumes in Medicina and Life Sciences

Series A: e-Books on Cardiovascular Diseases

                    6 Volumes

Series B: Frontiers in Genomics Research

                    2 Volumes

Series C: e-Books on Cancer & Oncology

                    2 Volumes

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

                    4 Volumes

Series E: Patient-Centered Medicine – LINKS to e-Books & Cover Pages for Volumes 1,2,3,4

                    4 Volumes

Advice offered by Conxa Catot · BDM, ex-Elsevier

 

  • Panamericana to offer the ebooks

https://www.medicapanamericana.com/

  • Springer Nature

https://www.springernature.com/la

  • Exlibris Group was purchased by Proquest

https://exlibrisgroup.com/

  • Proquest

https://about.proquest.com/

Ana Neira ana.neira@proquest.com and offer your ebooks and contents; she will forward you to the right contact in case they have interest.

  • ebook platform from Proquest

https://ebookcentral.proquest.com/

 

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e-Voices Podcasting by LPBI: Celebrating Podcast #3 in an Audio Library on leading Scientists and Key Opinion Leaders in Biological Sciences

Authors: Aviva Lev-Ari, PhD, RN

 

2.0 LPBI had published today its third Podcast uploaded to our Cloud at WordPress.com

https://pharmaceuticalintelligence.com/audio-podcasts/

  • Our three podcasts are about the careers of Leaders in Science and Medicine featured in LPBI’s emerging PODCAST LIBRARY, under development.
  • Podcasting Library represents an initiative of Dr. Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN as expressing on 1/25/2016:

Launching LPBI’s, Fourth Line of Business (D): FIVE Podcasts – Audio Series in BioMed

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/25/launching-lpbis-fourth-line-of-business-d-five-podcast-audio-series-in-biomed/

  • Since 2019 this initiative is under the leadership of Gail S. Thornton
  • Audio Podcasts represent Strategy #3 in 2020-2021 Vision for 2.0 LPBI
  • Development Plan for e-VOICES Podcasting, 2019 – 2021* is presented in

https://pharmaceuticalintelligence.com/audio-podcasts/

 

 Interviewer
Interviewees
Topic
yr
media
2019

&

2020
 
 
 
Gail S.
Thornton
Dr. Stephen J.
Williams,
Dr. Irina Robu &
Dr. Aviva Lev-Ari
3D BioPrinting in
Medicine
’19
Audio
Gail S.
Thornton
Prof. Feldman
Evolution Biology
and
Population Genetics
’20
Audio
Gail S.
Thornton
Dr. Larry H.
Bernstein,
MD, FCAP
Life Memoirs in
Clinical
Pathology 
’20
Audio
Narrator:
Dr. S.J. Williams
Gail S.
Thornton
Dr. Sudipta Saha
Research in
Reproductive
Technology
’20
Audio
2021
 Gail S.
Thornton
Dr. Justin D.
Pearlman,
MD, PhD
Cardiac Imaging –
Evolution of
Diagnostic
Methods
’21
Audio
Gail S.
Thornton
Dr. Raphael Nir
From Big Pharma to
Biotech
Entrepreneurship
’21
Audio
Gail S.
Thornton
Dr. Meg Baker
GlycoBiology in
Practice
’21
Audio
Gail S.
Thornton
Dr. Ofer Markman
Memories of
Procognia
’21
Audio
Gail S.
Thornton
Dr. Irina Robu
AI & Tissue
Engineering
’21
Audio
Gail S.
Thornton
Dr. Williams
Preferably 3rd 
party
interviewer
sourced
by Gail
AI & Genomics 
’21
Audio
Gail S.
Thornton
Dr. Aviva Lev-Ari
Preferably 3rd 
party
interviewer
sourced
by Gail
Curation & ML
’21
Audio

 

PODCAST #3:

Dear Dr. Saha,

I enjoyed very much the Podcast uploaded today on your Career in Biological Sciences.

Learning about your career by watching the Podcast was a special experience in comparison to reading your CV in 2012 and inviting you to join the Team of EAWs at LPBI.

  • I am so glad that I invited you then and had uploaded your CV to our Cloud on September 9, 2012 at 8:02 pm 
  • I am very happy that we have completed in BioMed e-Series: Volume #17 on Reproductive Genomic and Endocrinology 
  • I have initiated and launched that volume as an opportunity to put together the majority of your contributions in this domain on our Journal

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

Volume Four: Human Reproductive System, Genomic Endocrinology and Cancer Types

https://pharmaceuticalintelligence.com/biomed-e-books/series-d-e-books-on-biomedicine/series-d-volume-4-human-reproductive-system-genomic-endocrinology-and-cancer-types/

Book Structure

 

Sudipta Saha, PhD

Larry H. Bernstein,

MD, FCAP

Stephen J. 

Williams, PhD

Aviva

Lev-Ari, PhD, RN

TOTAL

# Articles

24

37

5

20

Enjoy the podcast

###

PODCAST #2:

Dear Dr. Larry,

Your PODCAST represents an amazing career in Medicine

I have uploaded your CV on Sep 9, 2012 at 19:59 pm

Since 2012 till the end of 2016 – you were 

  • LPBI’s Chief Scientific Officer, 
  • Author/Curator of 1,400 articles 
  • Content Consultant on 4 of our 5 e-Book e-Series 
  • Editor of 15 e-Books of our 17 e-Books
  • Single Author of 3 of the 17 e-Books

I am indebted to all your contributions to 1.0 LPBI’s Portfolio of Intellectual Properties

Enjoy the podcast!

Please click here to listen to an expanded life and times of Larry H. Bernstein, M.D. FCAP

###

 

PODCAST #1:

Dear Prof. Feldman,

Your podcast is carrying the voice of an amazing Scientist in Evolution Biology, 

among the top 10 World renown contributors to this field

 

I have uploaded your CV on  September 9, 2012 at 9:38 am

  • We met in 9/1987 when I was a newly minted Berkeley, PhD working at SRI International and you were and still are at Stanford University
  • You followed my career since then, ~35 years
  • I thank you for your contributions to LPBI and your support of my initiatives

Enjoy the podcast!

Future podcasts will feature scientists, clinicians and researchers behind some of the most exciting medical and scientific advances in the world. 

Podcast 2020-02-14 Gail Thornton Interviews Prof. Marcus Feldman by Leaders in Pharmaceutical Business Intelligence

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Announcing Strategic Transition from 1.0 LPBI to 2.0 LPBI on 1/1/2021: New Management, Marketing Communication and New Scientific/Technical Opportunities

Author: Aviva Lev-Ari, PhD, RN

 

For your New Opportunity in 2021: Management, MarkComm and Scientific/Technical

CONTACT

Aviva Lev-Ari, PhD, RN

Director & Founder

https://lnkd.in/eEyn69r

e-Mail: avivalev-ari@alum.berkeley.edu

(M) 617-775-0451

 

DESCRIPTION

https://pharmaceuticalintelligence.com/vision/

 

Announcement

Strategic Transition from 1.0 LPBI to 2.0 LPBI on

 1/1/2021

 

We have transitioned from

  • 1.0 LPBI was an electronic Scientific Publisher, 2012 – 2020

to

  • 2.0 LPBI a Medical Text Analysis (NLP-ML-AI) – SaaS and Content Monetization (Blockchain) – BaaS. A new company profile, 2021 – 2025

Our New NEEDS in Business Development and M&A – Pursue with results:

  • Equity Sharing based

Opportunities Map in the Acquisition Arena

Our New NEEDS in Marketing Communication, Media & PR – Produce new Digital mediums

  • Equity Sharing based
  1. The Announcement of 2.0 LPBI Launch
  2. NEW Website for 2.0 – Initiative #2
  3. Podcast – Strategy #4 – we wish to publish 2 Podcasts per quarter
  4. Planning Advertisement for Amazon Books using Amazon Advertising
  5. NEW documentation on Strategy #2 – Monetization of Journal Articles
  6. NEW documentation on Strategy #2 – Monetization of other IP Asset Classes
  7. NEW documentation on Strategy #1, #3, #6
  8. Reporting on developments in Strategy #5: Joint Ventures & Partnership

Our New Scientific/Technical Opportunities in Natural Language Processing (NLP), Machine Learning (ML) and Artificial Intelligence (AI)

 

Seeking 10 Students INTERNS for a major Medical Text Analysis using NLP, ML, AI

This is a ONE year VIRTUAL STUDENT INTERNSHIP for

  • Computer Science & Biological Sciences or
  • PreMed students
  • PostDocs will develop the interpretation for the hyper-graphs

This internship is not fee for service – but voluntary and offers

  • Mentorship by Scientists
  • Letter of recommendation
  • Opportunity to publish with lead authors
  • Hands on experience with software
  • Opportunities to present in corporate business meetings
  • Scientific Career guidance
  • Access to contacts in Academe and Industry

 

DESCRIPTION

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/

 

 

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Two Site Map Proposals for LPBI’s New Web Site

Curators: Gail S. Thornton, PhD(c) and Aviva Lev-Ari, PhD, RN

Site Map Version 1: Proposal by Aviva

2021-2025: 2.0 LPBI SITE MAP

2.0 LPBI:

  • Executive Summary
  • Vision
  • Brochure
  • Team’s Profiles on Knowledge Portals System

 

PRODUCTS, 2021 – 2025

  • Digital Text & Image Products

6,000 Articles

Article Abstract in WordClouds Images [2020 Summer Internship]

Gallery of +5,000 Biological Images

  • Audio Products: Development stage

Articles in SoundCloud files

Audio Books in Medicine

Biotech & Medical Conference e-Proceedings

Tweet Collections

  • BioMed e-Series Electronic Books in Foreign Languages: Development stage

Japanese

Spanish

Russian

 

SERVICES, 2021 – 2025

  • Insights as a Service [IaaS]: 2021 Medical Text Analysis using NLP, ML, AI
  • Blockchain as a Service [BaaS]: Content Trade using Transactions System for a Digital Store on a Healthcare Marketplace functioning as an Exchange 
  • e-Voices Podcasting
  • Drug Discovery using Synthetic Biology – JV
  • Customer Surveys

AI/ML/NLP PORTALS, 2021 – 2025

  • Genomics
  • Cancer
  • Cardiovascular
  • Metabolomics, Immunology & Endocrinology 
  • Coronavirus & Infectious Diseases
  • Precision Medicine
  • Blockchain in Healthcare

ABOUT 2021 – 2025

  • Testimonials
  • Board Members
  • Founder
  • Quarterly Newsletters: 1/1/2021
  • Current News: 1/1/2021 
  • Calendar: 1/1/2021
  • Connect With Us                            Email           LinkedIn

               Twitter

               Facebook

2012-2020 REFERENCE Record on 2.0 LPBI SITE MAP

https://pharmaceuticalintelligence.com/

1.0 LPBI: 

  • Executive Summary
  • Brochure
  • History
  • VISTA for Exit
  • Our Team of Contributors, 2012 – 2020
  • Investor Relations

 

  • DIGITAL PRODUCTS

Journal PharmaceuticalIntelligence.com: 6,000 articles

BioMed e-Series: e-Books

Press Coverage: e-Proceedings

Social Media Coverage: Tweet Collections

 

  • BUSINESS SERVICES 

Funding, Deals & Partnerships

Healthcare Investor’s Corner

 

ABOUT  2012 – 2020

  • Testimonials
  • Board Members
  • Founder
  • Quarterly Newsletters, 2018 – 2020
  • Current News, 2012-2020
  • Calendar, 2012-2020 
  • Contact us

@@@@@

 

Site Map Version 2: Proposal by Gail S. Thornton

Draft updated: 11/30/2020

Site Map

Connect With Us                  Search

           Email

           LinkedIn

           Twitter

           Facebook

 

Leaders in Pharmaceutical Business Intelligence Group

 

Note: Our Company, Our Services, Our Commitment are major buckets in this order.

 

Our Company                           Our Services 

            About Us                                           Pharmaceutical e-journal

            Leadership

                        Board Members                   AI / ML: Genomics, Cancer

                        Executive Team                    Biomed e-books

            History                                                          Audio and Foreign language 

            Locations                                          Conference e-Proceedings

            Testimonials                                     Podcasts

                                                                       Newsletters

                                                                       Coronavirus portal

                                                                       Medical text analysis

                                                                       Blockchain technology

                                                                       Calendar                                                                     

Our Commitment

            LPBI 2.0

                        Executive Summary

                        Vision

                        Brochure

                        Knowledge Portals

 

            LPBI 1.0

                        Executive Summary

                        Vision

                        Brochure

 

            Summer Internship Program

                        WordCloud article abstracts

 

            Drug Discovery

                        Joint ventures

 

            Investor Relations

            Health care Investors’ Corner            

            Customer Surveys

@@@@

Read Full Post »


Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V

Author: Aviva Lev-Ari, PhD, RN

 

 

UPDATED on 2/5/2020

Decision RULES:

  1. IF an article is in an e-Book THEN context for NLP is defined to be All articles in its Chapter in the Book
  2. IF an article is NOT in an e-Book THEN context for NLP is defined to be Articles in Main Research Category Top 12 by Views

Pending estimation of:

  1. Investment needed for Text Analysis with NLP 
  2. Investment needed for Content Monetization on Blockchain IT Infrastructure by vendor
  3. Investment needed for Text to Audio conversion
  4. Investment needed for Translation to Foreign languages
  5. Cost of translation of (e), below to several Foreign Languages
  6. Pricing EACH OUTPUT of NLP process: 

(a) WordCloud 

(b) Bar diagram 

(c) Hyper-graph

(d) Tree Diagram

(e) Expert Interpretation of (a) to (d)

UPDATED on 2/1/2021

At present, I see the following:

LPBI 1.0 – Blockchain LEDGER for Monetization of Class I, II, III, V

  • Custodian of the LPBI 1.0, 2012-2020 Portfolio of IP ten Assets Classes
  • For content monetization, we identified four of the ten assets: 

Class I: Journal articles, 

Class II: 18 Books, 

Class III: 100 e-Proceedings & Tweet Collections, 

Class V: +5,100 Biological Images

  • Content monetization requires a Blockchain Transaction Networks: Immutable ledger, permissions, smart contracts, recommendation engine

LPBI 2.0 – Blockchain LEDGER for Monetization of Graphics generated by ML and Experts interpretation in several Foreign languages

  • NLP, Machine Learning-AI applied for Text Analysis of Class I, II, III, V
  • Content monetization requires a Blockchain Transaction Networks

Economies of scale will be achieved by:

  • Development of one Content Promotion System
  • Unified IT Cloud-based infrastructure
  • Maintenance of B2C IT transaction system in a Digital Store at a Healthcare Marketplace [monthly fee paid for the use of the network and hosting content]
  • Installations of B2B at institution – pay per use vs subscription base

 

UPDATED on 1/28/2021

 

UPDATED on 1/27/2021 – Additional Observation

From: Amber 

Date: Thursday, January 28, 2021 at 11:21 AM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Re: Data Architecture for Blockchain Deployment of Digital Assets: LPBI IP Asset Classes I,II,III,V | Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Thank you, Aviva. This is consistent with my understanding as well. A couple of notes:

1. We can build the analytics that you described directly on the BurstIQ Platform; you do not need NLP to render these visuals (although you can certainly use NLP if you want to). The visuals can be presented in the marketplace either as a static image, or as a dynamic visual that changes based on how the user filters the data.

2. With respect to your note re: using one block for NLP: one block equals one piece of data, like a word cloud image or an author’s name. To incorporate NLP, we would integrate with the NLP services via a REST integration, so that the platform can both present data to the NLP service and ingest processed data from the NLP.  Then the output files from the NLP service would be stored in one or more blocks on the platform.

I hope that additional info helps.

Cheers,

Amber

We are still working to produce the 

  • INPUT two TEXT files for LINGUAMATICS to run their NLP
  • We will run on SAME Text our access to Wolfram’s NLP
  • On BurstIQ end: 
  • FOR OUR PROJECT – may be it is worth exploring having ONE block in the blockchain to be the processor of NLP – this is OUR IDEA for our own needs

We will get back to you as soon as we clarify which one runs supreme Linguamatics vs Wolfram 

We are to meet with CS CMU experts to clarify our specs about that interface that will be best:

  • Static Graphic files vs 
  • Graphic production on the fly by ONE NLP block on your Blockchain [That will need to be tested???? 

Observations:

  1. Advantage of static files – Graphics produced by NLP exist for Content Promotion and are available to the Recommendation Engine to display as a result of a query
  2. Advantage of compute on the fly – done on subset of article collection ON DEMAND not in existence in the statics files generated on 2 article sets: All articles in one Chapter and Same number of articles form the Main category of research
  3. BOTH MAY BE NEEDED TO EXIST ?????
  4. I assume each MODE of implementation has a difference I/O and overhead performance numbers and if Both exists these numbers may be x2 ????

PS

  • The first Quote was for Existing IP – 1.0 LPBI
  • The amended Quote [PENDING] – will be addition to consider the NLP Graphical output been ingress or created on fly or both (reasons, above, why both are needed). Graphical output from NLP are Content Products to be available on the Transaction infrastructure for download and monetizing of the IP involved

We are now designing the requirement for the Data Architecture for the blockchain Transaction Network for Content Monetization.

https://pharmaceuticalintelligence.com/2020/11/16/data-architecture-for-blockchain-deployment-of-digital-assets-lpbi-ip-asset-classes-iiiiii/

  • The unit case is an “Article” – a Longitudinal Profile of Classifiers
  1. Article has date of publication, 
  2. Author(s) Name, 
  3. Title, 
  4. Length, 
  5. URL 
  6. is it in a Book? 
  7. Series, Volume, Chapter; 
  8. Views end of each year since published
  9. is the article a Conference output or not; 
  10. if yes Name of Conference, date, location, 
  11. is it part of e-Proceedings? 
  12. If yes Title & URL; 
  13. Does a Tweet Collection for this Conference exist? 
  14. If yes Title & URL
  • Each of the is a columns added in an Excel file FOR the same article in one row A to Z
  • Same is repeated for Row 2 – A to Z for article #2 
  • End of Rows is +6,000
  • End of Columns is Last Classifier, 1 to n
  • The Views per article times length of article # Words = Score for Authors contribution times all article by same Author = Total score for potential compensation AFTER Exit.

Currently, for performing NLP:

  • The content – is an MS Word file of the article 
  • It is INGRESS to a platform that has Natural Language Processing [NLP] Algorithms on it
  • Semantic Text Analysis is Performed
  • NLP system generate Graphical OUTPUT 
  1. WordCloud, 
  2. Bar Diagram for Word frequency, 
  3. Hyper-graph for concept relations, 
  4. Tree Diagram for hierarchical affinity translated into distance proximity among words; 
  5. Domain Knowledge Expert writes Interpretation of the Graphs

FUTURE

  • These Graphical OUTPUTS EGRESS the NLP platform
  • These Graphical OUTPUTS will INGRESS the Blockchain Transaction infrastructure
  • That interface NEED to be design on several layers. For our ability to declare our SPECS on that we will meet with experts from CS @CMU 
  • LPBI does not have enough expertise onboard at that level of data engineering, data workflow & system design to be able to submit specs.

 

UPDATED on 1/27/2021 – This update deals with Integration of NLP Graphical output on a Blockcahin transaction network IT infrastructure

Our content is in Life Sciences, Pharmaceutical, Healthcare, Medicine, Medical Devices

1.0 LPBI IP Portfolio of an e-Scientific Publisher – 3.3 Giga bites of English text and Biological graphics

  • 6,000 scientific Journal articles – curations of peer reviewed scientific findings – the clinical interpretation written by experts.
  • 18 Books in Medicine and Pharmaceutics
  • 100 e-Proceedings of Medical and Biotech top Global Conferences we covered in realtime on PRESS passes and Tweet Collections from 36 events
  • 5100 Biological images used in the articles above

2.0 LPBI IP Portfolio of a Medical Text Analysis w/ Machine Learning-AI (SaaS) and Content Monetization Blockchain company: BaaS.

We plan to apply Natural Language Processing, ML-AI on that content for Semantic Medical Text Analysis on 1.0 LPBI IP portfolio, listed above and generate graphical representation of the semantic relations:

  • WordClouds
  • Hyper-graphs
  • Tree Diagrams
  • Domain Knowledge Interpretation of Graphical output of NLP, ML-AI

Our Proof-of-Concept is on–going 

  • Interested party in NLP on our content in Genomics & Cancer is a Healthcare Insurer in UT.
  • We are interested in NLP on ALL our content: Cardiovascular, Genomics, Cancer, Immunology, Metabolomics, Infectious Diseases, Genomic Endocrinology and Precision Medicine – our 18 books in medicine, average book size 2400 pages ~ 1800 articles in the entire BioMed e-Series and the 4200 articles in the Journal not in Books
  • We are interested in content monetization of the
  1. Content in Text format, and of the
  2. Digital graphical products generated by NLP 
  3. Domain Knowledge Experts interpretations of the Graphical output of NLP
  4. These Interpretations of the digital graphical products generated by NLP are and will be a fundamental resource for consultancy of drug discovery, drug repurposing , drug substitution. Team of 10.

External Relations:

NLP 

  • LINGUAMATICS / IQVIA will run on their NLP system our test sample TEXT files and we are using internally Wolfram for Biological Sciences 
  • We will compare the two graphical outputs: theirs and ours 

Blockchain

We work with a leader in Blockchain IT vendor in Colorado on the design of a cloud-based Transaction Network IT infrastructure for content monetization taking place on an IT system with Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine

Two types of markets will be served: 

  • B2C – a digital store in a Healthcare Digital Marketplace for 1.0 LPBI IP Portfolio and 2.0 LPBI IP Portfolio
  • B2B – Special installations at Big Pharma R&D and at Healthcare Insurers

2.0 LPBI IP Portfolio and strategy represent the first implementation ever done of

NLP on a Blockchain backbone

[we were told so by the leader in NLP and by the leader in Blockchain]

We explore to discuss our plans with with additional experts from CS at CMU 

  • Experts on NLP 
  • Experts on Blockchain Transaction Network
  • We need to decide on between two designs considered for the interface between NLP & Blockchain
  • The interface is related to two methods of input graphic data processing: (a) ingress NLP outputs to the blockchain system from a DB vs creation of NLP graphic products on the fly
  • We need to discuss the System design and the data architecture with CMU experts in both fields: NLP & Blockchain
  • We will need expert assistance in defining each of the Blockchain features: Permissions, Smart Contracts, Immutable Ledger, Recommendation Engine Rule Base

Business Side

  • We are seeking new ownership
  • We are seeking new management
  • Scaling up from the proof-of-concept to commercialization and content monetization represents a scale of operation that is beyond us. 
  • We have a VAST IP Portfolio and a Team of Experts N=10
  • We are the creators of the IP portfolio of 1.0 LPBI – 3.3 Giga bites
  • We are the creators of the Vision for 2.0 LPBI IP 

Strategy #1: NLP for Text analysis of 1.0 LPBI content and 

Strategy #1: Content monetization on Blockchain IT Transaction network: Original Content and NLP digital graphical products

  • All the content is in the Cloud hosted by Wordpress.com
  • PharmaceuticalIntelligence.com is the Domain Name – it is listed on my own name. Formula for post-Exit compensation of Experts, Authors, Writers of the 6,000 articles is in place.

UPDATED on 1/26/2021 – This Update is on “The unit case is an “Article” – a Longitudinal Profile of Classifiers”

The unit case is an “Article” – a Longitudinal Profile of Classifiers

  • It has date of publication, Author(s) Name, Title, Length, URL is it in a Book? Series, Volume, Chapter; Views end of each year since published; is it a Conference or not; if yes Name of Conference, date, location, is it part of e-Proceedings; is there a Tweet Collection for that Conference?
  • The content – an MS Word file of the article is INGRESS by a platform that has Natural Language Processing [NLP] Algorithms on
  • Semantic Text Analysis is Performed
  • Graphical OUT is created and EGRESS:
  1. WordCloud,
  2. Bar Diagram for Word frequency,
  3. Hyper-graph for concept relations,
  4. Tree Diagram for hierarchical affinity translated into distance proximity among words;
  5. Domain Knowledge Expert writes Interpretation of the Graphs
  • Each of the is a column added in an Excel file FOR the same article on one row in (i to n) columns
  • Same is repeated for Row 2 – (i to n) columns for article #2 
  • End of Rows is +6,000
  • End of Columns is Last Classifier, n
  • The Views per article times article length = Score for Authors contribution times all article by same Author = key score for potential compensation AFTER Exit.
  • ORIGINAL Excel file on Article Views has the VIEWS data organized as a Classifier in a LONGITUDINAL Article profile

 

UPDATED on 1/18/2021 – adding data fields or DBs for Content monetization

The hyper-graphs and the Tree Word are including all words – that does not affect the revealed SIGNIFICANT words.

  1. We include all of the NEW runs in the POWERPOINT Presentation

We need to present YOUR PowerPoint on 

  • 1/20 Zoom with NLP Vendor
  • 1/22 Zoom with Blockchain Vendor

All the iterations are needed for as to test the concepts of the 16 articles – ALSO on

A. One article and all the OTHER articles in ONE CHAPTER in ONE Book, I.e., Genomics Volume 1, Chapter 1

B. One article and other articles included in the MAIN Research Category this article was assigned to by the Author

We will need Hyper-graphs and Tree Diagrams for A and for B, above – THEN

  • we will decide on 2.0 LPBI standard: Hyper-graphs or Tree Diagrams as the INPUT for Domain Knowledge Expert’s Interpretation.

C. Announcing Proof-of-Concept for Genomics and Cancer is COMPLETE and CLOSED.

D. Enumeration of all artifacts in one “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [by Code Author: Madison Davis]

  1. WordCloud
  2. Bar graph
  3. Hyper-graph or Tree Diagram – ONE to be decided to make to the Standard
  4. Text – Interpretation by Domain Knowledge Expert for 1,2,3, above

E. Announcement of Scaling up Project by BioMed e-series: A, B,C, D, E

  • using the “STANDARD 2.0 LPBI Medical Text Analysis OPERATION” [Standard was developed by the Proof-of-Concept.

 

UPDATED on 1/18/2021 – adding features to Content monetization

We are 2.0 LPBI

1. Medical Text Analysis

2. Content monetization

IF

3rd party requests services we did in 1.0 LPBI

THEN

We offer the service for a fee and the monetization will be held by the Blockchain transaction system

Thus, we need to guide our IT Vendor designer of our Blockchain features platform to DESIGN the LEDGER to include few additional categories such as:

1. Consulting Services – Fee for Service

Types of Service:

1.1 Implementation of Medical Text Analysis for Pharma

1.2 Implementation of Medical Text Analysis for Healthcare Insurers

2. Response by 2.0 LPBI to Requests to promote content by 3rd party: 

2.1 Co-marketing of a Conference organized by 3rd Parties – promotion on LPBI Channels

2.2 LPBI to Publish 3rd Party contents, i.e., Articles by guest authors: Payment based on # of views every 90 days at $30 per view

3. Consulting on Media development

3.1 Conference organization

3.2 Book content development

3.3 Real time Press coverage

UPDATED on 1/13/2021

  • We will have from our IT Vendor a BLUEPRINTS for the content monetization system design with all the components laid out in a workflow for a production process to incorporate two sources of data:

1.0 LPBI four IP Asset classes: I, II, III, V will be available for monetization 

The Design include all monetization Features to incorporate the 2.0 LPBI NEWLY TO BE CREATED PRODUCTS by NLP integrated at the article level with the 1.0 LPBI IP.

We will generate four Text Analysis products, like the FOUR outcomes of NLP included in the Proof-of-Concept: 

NLP Products: Will be available for monetization as 2.0 LPBI IP: 

  1. WordClouds, 
  2. Bar charts, 
  3. Hyper-graphs and 
  4. Expert Interpretation in English and Foreign Languages

PHASE I: All Articles in ALL Books at the Chapter Level – THEY WILL HAVE: 

  1. WordClouds, 
  2. Bar charts, 
  3. Hyper-graphs and 
  4. Expert Interpretation in English and Foreign Languages

For:

Series A:  6 volumes, 

Series B:  2 volumes

Series C:  2 volumes

Series D:  4 volumes – 1, 2&3 in one Book, 4

Series E:  4 volumes

Total 17 Books for 18 Volumes

PHASE II: All Articles Not in Books and Not as e-Proceedings – – THEY WILL HAVE: 

  1. WordClouds, 
  2. Bar charts, 
  3. Hyper-graphs and 
  4. Expert Interpretation in English and Foreign Languages

PHASE III: 60 e-Proceedings + 36 Tweet Collections – – THEY WILL HAVE: 

  1. WordClouds, 
  2. Bar charts, 
  3. Hyper-graphs and 
  4. Expert Interpretation in English and Foreign Languages

PHASE IV: 5,100 Biological Images -– THEY WILL HAVE: 

  1. WordClouds, 
  2. Bar charts, 
  3. Hyper-graphs and 
  4. Expert Interpretation in English and Foreign Languages

UPDATED on 1/5/2021

  • WE ARE ARE DOING THE PROOF-OF-CONCEPT in house with INTERNS on a one year Internship on a volunteer basis.
  • My intent was to TEAM UP with AWS and one of their PARTNERS to REDO the POC on the VERSION that XXX has in the NLP Software and with that Partner jointly to Present to the INSURER and secure a contract for that PARTNER that will scale up from
  • (a) 16 articles on Genomics to Volume 1 and Volume 2 Genomics Books and
  • (b) 16 articles on Cancer to Volume 1 and Volume 2 Cancer Books.
  • Hoping in the following phase of the relations with the INSURER –
  • they will be interested in all medical indications covered in our 16 Books (#17 due 1/11/2021) – Namely, they have Patients with Heart problems – LPBI has 6 Volumes in Cardiovascular, books on Immunology, Infectious disease, Metabolic, Endocrine and 4 volumes on Precision Medicine.
    • We mean to use the POC as a Lead toward having the INSURER involved in performing Medical Text Analysis on our 17 books
    • Since they will be the first to get access to the outcomes of such a massive NLP, ML-AI on 17 books
    • They will get access to Hyper-graphs and Domain Expert Interpretations for their INTEREST in Drug substitution and Cost containment and access to our TEAM for ad hoc genomics challenges.
  • The full scale implementation of the POC on all the content in the books requires a PARTNER with expertise and a platform for NLP 
  • It was my intent to find that PARTNER at XXX and its system of Partnerships
  • Our alternative is to Team up with another player in the NLP arena that is not AWS – in the case that XXX can’t team us up with their NLP capabilities
  • WE have approached XXX because our architecture REQUIRES INTEGRATIONS OF THE RESULTS on Medical Text Analysis
  1. WordCloud (Images files),
  2. Hyper-graphs (graph files),
  3. Interpretation of Hyper-graphs (Text file in English and in several Foreign Languages)
  • WITH A CONTENT MONETIZATION SYSTEM that is to be designed for our journal articles, Books, e-Proceedings, Tweet Collections, Biological images

 

  • Such an Integration will allowing for a

Customer to be able to request to review

(a) articles on Topic x

(b) receive from the system 12 top articles

(c) select one or more

(d) pay for them

(e) download the articles they paid for

Expand (a) to (e) to Books, e-Proceedings, Tweet Collections, Biological images

(a) to (e) represents 1.0 LPBI IP

 

  • Such an Integration will allowing for a

Customer to be able to request to review 

(f) WordClouds = Article ABSTRACTS

(g) Hyper-graphs

(h) Domain Expert Interpretations

(I) Interpretations in Few Foreign Languages

Customer will receive from the RECOMMENDATION engine 12 WordClouds of related top articles

Customer will receive from the RECOMMENDATION engine 12 Hyper-graphs of related top articles or one or more research categories

(j) Customer will select one or more

(k) pay for them

(l) download the WordClouds they paid for

(m) Download the HyperGraph they paid for

(n)  Download the Domain Expert Interpretations for the hyper-graph(s)

(o)  Select for the Interpretations to be in one of Few Foreign Languages the system offer

 (j) to (o) represents 2.0 LPBI IP

THE NEEDS OF LPBI IS for ONE INTEGRATED SYSTEM THAT CONTAINS:

(a) to (e) represents 1.0 LPBI IP

AND

(j) to (o) represents 2.0 LPBI IP

AND

CONTENT MONETIZATION SYSTEM with features such as:

PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

It may be the case that YYY has competence in monetization system design BUT DOES NOT currently have what LPBI needs in the Text Analysis with NLP, ML-AI

  • As a result XXX needs to pair us up with one additional XXX-Partner in the space of Text Analysis with NLP, ML-AI – to understand our requirements and to enable scaling up from POC to all the 17 Volumes in Medicine
  • YYY’s Monetization design needs to be INTEGRATED with the the system design for Text Analysis with NLP, ML-AI done by a second AWS partner
  • THEN
  • Hosting on XXX needs to be discussed
  • LPBI’s IP Asset Classes: I,II,III,V –  journal articles, Books, e-Proceedings, Tweet Collections, Biological images – FIT very well AWS Marketplace
  • Please introduce us to the XXX contact for discussion on LPBI and XXX Market place
  • See, Priority #3, Below and due to Priority #1 & #2
  • It seems to be the case that the DEVELOPMENT efforts are expansive for a venture like LPBI, therefore I requested to receive a POINTER to the XXX Venture Acquisition department/team/one person

 

  • Aviva: We need a Partner to Use our Content and use NLP, ML-AI to execute the SEMANTIC Medical Text Analysis to convert TEST to WordClouds and to Hyper-Graphs
  • if YYY can declare expertise in the Medical Text Analysis with NLP, ML-AI
  • If not, XXX may introduce us to another XXX Partner that can handle for LPBI Priority #1, below

 

  • Aviva: We need a Partner to design CONTENT MONETIZATION for existing content AND for the RESULTS of the Medical Text Analysis

 

EXPLANATIONS:

All of the above MUST bring all parties to an understanding of the NEEDS that LPBI has:

PRIORITY #1:

Medical Text Analysis using NLP, ML-AI

  1. LPBI has a Proof-of-Concept in Medical Text Analysis using NLP, ML-AI – will be completed mid Feb. 2021
  2. LPBI has a Client – a Healthcare Insurer interested in Genomics and Cancer and potentially, because they are also a HMO, in all other medical indications covered in LPBI BioMed e-Series – 17 BOOKS
  3. To present to this client (and to other Healthcare Insurers) – LPBI needs one  IT Partner in Medical Text Analysis using NLP, ML-AI able to GET a contract from the INSURER for using the POC to SCALE UP to 2 books in Genomics and 2 books in Cancer – desirable – to be followed up by the remaining (17 – 4) = 13 Books

PRIORITY #2 and PRIORITY #3: need to be running in parallel

PRIORITY #2

DESIGN and ENABLEMENT of Content Monetization for

(a) EXISTING digital products and

(b) the results of PRIORITY #1, above: Medical Text Analysis using NLP, ML-AI

  1. LPBI needs a Content Monetization System (CMS) that we believe YYY has the competences to design
  2. Continuing of progress on this design need to take place
  3. LPBI needs a Proposal and costs of monetization system design for presentation to IB and other funding sources
  4. LPBI is anticipating 3rd parties that will invest in IT infrastructure development.
  5. LPBI created a e-Scientific Publishing venture second to none – based on ~2MM Views has projected revenues to $ZZZ MM
  6. The Content Monetization Cloud-based IT System DESIGN needs to satisfy the following:
  7. THE NEEDS OF LPBI are of ONE INTEGRATED SYSTEM THAT CONTAINS:

[(a) to (e) represents 1.0 LPBI IP] – existing products 
AND 
[(j) to (o) represents 2.0 LPBI IP] – to be developed by NLP, ML-AI of the existing products
AND 
ENABLES CONTENT MONETIZATION of the two sources with features such as:
PERMISSIONS, LEDGER, RECOMMENDATION ENGINE

PRIORITY #3

DESIGN of CONTENT PROMOTION campaigns

  1. XXX Advertising is a company of XXX.com
  2. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaigns for (a) to (e) represents 1.0 LPBI IP [digital products: journal articles, e-Proceedings, Tweet Collections, Biological images]
  3. Upon progress with (j) to (o) represents 2.0 LPBI IP = the results of Text Analysis with NLP, ML-AI 
  4. We need to be teamed up with a Partner or an inside Group to XXX for the DESIGN of CONTENT PROMOTION campaign for WordClouds, Hyper-graphs and Domain Expert Interpretation of the Hyper-graphs in foreign languages

 

UPDATED on 1/4/2021

SPECIFICATION for the Road Map toward an Architecture for Monetization of Content at LPBI

1 – Data entry done by 2.0 LPBI Team of Interns 

2 – Data entry done by IT Vendor

3 – Architecture will be for monetization of 1.0 LPBI IP Asset Classes I,II,III,V

and for

4 – Architecture will also include the infrastructure for the data generated by Medical Text Analysis with NLP, ML, AI done on 1.0 LPBI IP Asset Classes I,II,III,V – called Results of Text Analysis

5. Results of Medical Text Analysis with NLP, ML, AI will include the following Databases (DB):

PHASE I: 

IP Asset Class II – e–Books

  • WordClouds for all articles in 17 BioMed e-Series BOOKS – [Image file – DB]
  • Number of words of which each WordCloud was built on [Text file – DB]
  • Hyper-grapah for articles in each Chapter in the book [Graph file – DB]
  • DomainExpert interpretation of the Hyper-graphs  [English Text file – DB]

1. TITLES of each article in the eTOCs of a Book across all books will be TRANSLATED into Spanish, Japanese, Russian [Text file – DBs, one per language]

2. One page of Domain Expert interpretation of the Hyper-graphs will be TRANSLATED into Spanish, Japanese, Russian  [Text file – DBs, one per language]

 

PHASE II:

Scale up PHASE I – from IP Asset Class II [all articles in 17 Books]  TO all the articles in the Journal = IP Asset Class I

 

PHASE III:

Scale up from PHASE I: from All Books (IP Asset Class II) and PHASE II: all the articles in the Journal (IP Asset Class I)

TO 

  • IP Asset Class III (e-Proceedings/Tweet Collections), 

 

PHASE IV:

  • IP Asset Class V (Biological Images)

 

UPDATED on 1/2/2021

Announcing Strategic Transition from 1.0 LPBI to 2.0 LPBI on 1/1/2021: New Management, Marketing Communication and New Scientific/Technical Opportunities

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/01/01/announcing-strategic-transition-from-1-0-lpbi-to-2-0-lpbi-on-1-1-2021-new-management-and-new-technical-opportunities/

 

We have transitioned from

  • 1.0 LPBI was an electronic Scientific Publisher, 2012 – 2020

to

  • 2.0 LPBI a Medical Text Analysis (NLP-ML-AI) – SaaS and Content Monetization (Blockchain) – BaaS.

 

  • A new company profile, 2021 – 2025

 

Content Monetization has TWO distinct parts:

 

2.1   Belongs to 1.0 LPBI: exist in WordPress.com cloud EXISTING digital IP asset classes: Articles, Books, e-Proceedings/Tweet Collections, biological images

2.2   Belongs to 2.0 LPBI: will be created by Text Analysis with NLP. ALL NEW TO BE CREATED digital IP asset classes by 2.0 LPBI as a result of Strategy #1: Text Analysis using NLP, ML, AI:

2.2.1 WordClouds – a DB of all images created by NLP one per article. This will be IP Asset Class 11, will belong to 2.0 LPBI (1 to 10, exist and belong to 1.0 LPBI)

2.2.2 Hyper-graphs – a DB of all graphs,  the hyper-graphs created by NLP. This will be IP Asset Class 12, will belong to 2.0 LPBI.

Examples:

  • One hyper-graph for articles in a Book Chapter x 20 Chapter per one book x 17 books
  • One hyper-graph for articles in Categories on the Journal ontology
  • N=730 categories

2.2.3 English Text interpretation of each Hyper-graph – a DB of text Interpretations linked to DB of graphs and DB of Images. This will be IP asset Class 13, belongs to 2.0 LPBI

These Text interpretations of hyper-graphs will be translated to foreign languages. Example, Spanish, Japanese

ONE DB of Text interpretations per one language

 

2.0 LPBI had several IT infrastructure needs:

A.  Infrastructure for Text Analysis with NLP of all IP assets in 2.1

B.  Monetization infrastructure for IP Assets of 2.1, above

C.  Monetization infrastructure for IP Assets of 2.2, above

 

System integration of A, B, C

My understanding is that you wish to address B.

Leaving A and C for later.

My view is:

  • B and C are one project because a USE CASE called A Journal Article Profile needs to have all the data fields I covered, in the e-mail, below 2.1 plus 2.2, as above. The architecture for B and C are inseparable – Meta data needs to be comprehensive
  • A – Infrastructure for Text Analysis needs to be developed in parallel to the Content Monetization B and C.

If All what 2.0 LPBI will do will be

  • monetization of Content generated, 2012-2020 – it’s valuation will be x

Versus

2.0 LPBI

(a) A Medical Text Analysis Company – SaaS and a

(b) Content Monetization Company – Blockchain as a Service (BaaS)

 

2.0 LPBI distinct competitive advantages are:

  1. we created content we own it vs applying NLP on PubMed.
  2. we create Value-Add by NLP with Expert INTERPRETATION in multi languages
  3. We monetize digital content
  4. We monetize WordClouds “image files and Hyper-graphs “graph files”

 

System Integration job needed for 2.0 LPBI includes the following:

  1. Our IP on WordPress needs to be migrated into a Cloud Computing environment of an INTEGRATOR i.e.,
  • AWS
  • DELL
  • Other
  1. That integrator needs to have the two technologies we need:

Strategy #1: Text Analysis by ML

  • Medical Text Analysis SW: NLP, ML, AI

This is Strategy #1 for 2.0 LPBI, namely

Conversion of 3.2 Giga bites of English Text into Hyper-graphs of Semantic content relationships for applications such as:

– drug discovery (needed by Big Pharma)

– drug repurposing (needed by Big Pharma)

– drug substitution & cost containment (needed by Healthcare Insurers)

 

Strategy #2: Content Monetization by Blockchain IT infrastructure features:

  • Permission granting to download content on a cyber-secure IT platform
  • immutable LEDGER – recording payments
  • Recommendation Engine: choose one or more article from this list of 12
  • Blockchain SW: Transaction network for Ledger, Immutability, Recommendation engine and Permission to download

This is Strategy #2 for 2.0 LPBI, namely

Content monetization requires IT infrastructure

We understand that 2.0 LPBI need to

  • partner

or

  • be acquired by a 3rd party

(a) to invest in the IT needed for content monetization of

1.0 LPBI IP asset classes: I, II, III, IV

2.0 LPBI IP novel asset classes such as

IP Asset Class 11: WordClouds

(image file DB)

IP Asset Class 12: Hyper-graphs

(graph file DB)

IP Asset Class 13: Domain Expert interpretation of Hyper-graphs

(text file DB, one DB for a Language, expert interpretation translated in several languages)

 

  1. 2.0 LPBI Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and

  1. 2.0 LPBI Strategy #2:  Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS))

and

  1. LPBI & A2C-AWS regarding Strategy #1: NLP
  2. LPBI & A2C-AWS regarding Strategy #2: Monetization

I believe that the definition for the Profile of an Article I am providing below will clarify matters more and your feedback will be helpful.

1.0 LPBI had created 6,000 articles in need for monetization

2.0 LPBI is launching Six new initiatives the relations of four of the six are tied with the definition of an Article PROFILE, as below.

  • The monetization INFRASTRUCTURE needs to accommodate TWO types of Digital Products:

(a) The existing Journal articles

(b) The RESULTS generated from Journal articles being subjected to TEXT ANALYSIS with NLP, ML, AI

Therefore we need to address:

C.  LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

D.  LPBI & A2C-AWS regarding Strategy #2: Monetization

 

Let’s start with C.

LPBI & A2C-AWS regarding Strategy #1: NLP on 1.0 LPBI Text

 

It seems that AWS has technologies in place for A2C to use for performing Medical Text Analysis using AWS NLP, ML, AI on 1.0 LPBI’s 6,000 articles

– Thus, we need to explore HOW we can use AWS NLP, ML, AI technologies and produce for 2.0 LPBI the following Text Analysis features:

[they are derived from our Proof-of-Concept is on–going]

5.3 Does the article have the Text Analysis features which are obtained by performing text analysis with NLP:

5.3.1.  a WordCloud – needs to be stored in graph file of WordClouds

5.3.2.  # words used

5.3.3.  Hyper-graphs – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

 

Let’s continue with D.

LPBI & A2C-AWS regarding Strategy #2: Monetization

A2C will design a Cloud-based IT Infrastructure that will enable monetization of two types of products:

Type One: 1.0 LPBI Asset Classed I, II, III, V

  • Below is the Profile Definition for the Unit Case: A Journal Article (1.0 LPBI Asset Classed I) – See below
  • Same Profile Definitions needs to be done for 1.0 LPBI Asset Classed II (books), III (e-Proceedings/Tweet Collections), V (Gallery of 5,100 Images) – PENDING

Type Two: POST Medical Text Analysis using NLP, ML, AI – the following NEW PRODUCTS are created and NEED TO BE MONETIZED

Text Analysis features to be produced by NLP, ML, AI:

5.3.1. a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4. Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – performed by 2.0 LPBI Experts generating Text files

5.3.4.2 Domain Expert interpretation for 5.3.3.2 – performed by 2.0 LPBI Experts generating Text files

BASED on the definition provided, below, suggested steps by 2.0 LPBi are the following:

  • A2C-AWS and 2.0 LPBI will generate a PROPOSAL for AWS to fund that effort for future placement in AWS Marketplace
  • A2C-AWS and 2.0 LPBI will develop Plans and Cost Structures of the infrastructure needed for CONTENT monetization – to be presented to Investment Banker in NYC
  • A2C-AWS and 2.0 LPBI will take the LPBI Proof-of-Concept on Medical Text Analysis with NLP in Genomic and Cancer and will create jointly TWO skeleton IT Structures

#1 Skeleton IT Structure: 

Reproduce the Proof-of-Concept using AWS – NLP–ML-AI technology and Scale up to One Chapter in Genomics Volume 1 and One Chapter in Cancer Volume 2

That will be JOINTLY presented at a Healthcare Insurer [LPBI’s Contact] by LPBI AND A2C-AWS – with the scope of getting a Contract that A2C-AWS will define, execute and manage the Statement Of Work (SOW) and submit Costs to the Healthcare Insurer. Prospects of expansion to Cardiovascular and Immunology, beyond Genomics and Cancer are strong.

 

#2 Skeleton IT Structure: 

Produce a Skeleton for Monetization of 

  • 0 LPBI – Journal articles AND 
  • 0 LPBI — the Results of #1 Skeleton IT Structure: PRODUCTION OF FEATURES of TEXT ANALYSIS using AWS NLP technologies

That will be presented at 

  • an Investment Banker in NYC [LPBI’s Contact], 

and 

  • by LPBI 

to other funding sources, and 

  • by A2C-AWS to other funding sources, Chiefly, AWS – internally.

             

The Opportunities MAP written on 2/2019 for LPBI M&A or Exit include

Twelve Economic Segments for LPBI Group’s IP – Prospects for Transfer of Ownership

  1.     Holding Companies, Investment Bankers and Private Equity
  2.     Information Technology Companies – Health Care
  3.     Scientific Publishers
  4.     Big Pharma
  5.     Internet Health Care Media & Digital Health
  6.     Online Education
  7.     Health Insurance Companies & HMOs
  8.     Medical and Pharma Associations
  9.     Medical Education
  10.     Information Syndicators
  11.     Global Biotech & Pharmaceutical Conference Organizer
  12.     CRO & CRA 

Information Technology Sector: Cloud-based – 

Amazon Web Services (AWS),  Alphabet – Verily, Apple-Health,  IBM Watson

Information Technology SectorCloud & Server-based – 

Microsoft-Health, Dell Boomi, Oracle-Health, SAP, Intel-Health

 

Please review this LINK:

https://pharmaceuticalintelligence.com/2019-vista/opportunities-map-in-the-acquisition-arena/

 

For the DESIGN of IT Infrastructure for Monetization, the following is an essential 

DEFINITION of a USE CASE for “PROFILE of an Article”: 

 

1.0 LPBI BEGINS 

Monetization of 6,000 Digital Products – USE CASE: A Journal Article

5.0 Article Title

5.0.1 Article URL

5.0.2 Author 1: Name

5.0.2.1 Author 2: Name

5.0.2.2 Author 3: Name

5.0.2.3 Author 4: Name

5.0.3  Date of Publication

5.0.4  # Words

5.0.5  # Views since Published to DATE

 

5.1 Is the article in a Book?

5.1.1 Article is not in a Book only in the Journal

5.2 Article is in a Book – In which one(s)?

5.2.1 LPBI Series A

5.2.1.1 Volume 1

5.2.1.2 Volume 2

5.2.1.3 Volume 3

5.2.1.4 Volume 4

5.2.1.5 Volume 5

5.2.1.6 Volume 6

5.2.2   LPBI Series B

5.2.2.1 Volume 1

5.2.2.2  Volume 2

5.2.3   LPBI Series C

5.2.3.1 Volume 1

5.2.3.2 Volume 2

5.2.4   LPBI Series D

5.2.4.1 Volume 1

5.2.4.2 Volume 2

5.2.4.3 Volume 3

5.2.4.4 Volume 4 [Dr. Williams and Dr. Irina are adding editorials, NOW]

5.2.5   LPBI Series E

5.2.5.1 Volume 1

5.2.5.2 Volume 2

5.2.5.3 Volume 3

5.2.5.4 Volume 4

 

1.0 LPBI ENDS

2.0 LPBI BEGINS

Strategy #1: Medical Text Analysis (NLP, ML, AI) (SaaS)

and 

Strategy #2: Monetization of Text Analysis Results as Products (Blockchain as a Service (BaaS)

5.3 Does article have the Text Analysis features:

5.3.1.a WordCloud – needs to be stored in graph file of WordClouds

5.3.2. # words used

5.3.3. Hyper-graphs  – need to be stored in graph file of Hyper-graphs

5.3.3.1 One Hyper-graph for All articles in a Book Chapter

5.3.3.2 One Super-graph for All articles in one or more Categories of Research  – need to be stored in graph file of Super-graphs

5.3.4.  Domain Expert interpretation for 5.3.3.

5.3.4.1 Domain Expert interpretation for 5.3.3.1 – Translated into few other languages

5.3.4.2 Domain Expert interpretation for 5.3.3.2 -– Translated into few other languages

 

5.4 Audio File added to Article

5.4.1 In place – Audio file type [Text to Audio]

5.4.2 SoundCloud file

 

5.  Article Titles was translated to

5.5.1   Spanish

5.5.2   Japanese

5.5.3   Russian

 

6.   Article Interpretation of Hyper-graphs was translated to

5.6.1   Spanish

5.6.2   Japanese

5.6.3   Russian

The content below was not Updated on 1/2/2021

Distinction between A and B, below

 

  • A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

  • B.  2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

 

A.  1.0 LPBI – 2012–2020 – IP Assets available for sale

 

A.1 A List of Scientific articles N=6,000 

STORED in Excel file run on 6/30/2020 and 12/31/2020

 

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research, see article example , below

For the Cancer category

  • we have the following tree structure
  • System had data on how many articles are in each category
  •  Cancer – General
  •  Cancer and Current Therapeutics
    •  interventional oncology
      •  Breast Cancer – impalpable breast lesions
      •  Prostate Cancer: Monitoring vs Treatment
  •  CANCER BIOLOGY & Innovations in Cancer Therapy
    •  Anaerobic Glycolysis
    •  Cachexia
    •  Cancer Genomics
      •  Circulating Tumor Cells (CTC)
        •  Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood
          •  mRNA
        •  MagSifter chip
      •  KRAS Mutation
      •  Li-fraumeni syndrome.
      •  TP53 – Germline mutations
    •  cancer metabolism
    •  Funding Opportunities for Cancer Research
    •  Genomic Expression
    •  Glioblastoma
    •  Hexokinase
    •  Loss of function gene
    •  Metabolic Immuno-Oncology
    •  Metastasis Process
    •  Methylation
    •  Microbiome and Responses to Cancer Therapy
    •  Monoclonal Immunotherapy
    •  mtDNA
    •  Oxidative phosphorylation
    •  Pancreatic cancer
    •  Pyruvate Kinase
    •  The NCI Formulary
    •  tumor microenvironment
    •  Warburg effect
  •  Cancer Informatics
  •  Cancer Prevention: Research & Programs
  •  Cancer Screening
  •  Cancer Vaccines: Targeting Cancer Genes for Immunotherapy
    •  Engineering Enhanced Cancer Vaccines

A.1.4  Type of article: by the role of the author: 

  • If the Author is Curator THAN this article is a curation
  • If the Author is Reporter THEN this article is a Scientific reporting article

A.1.5  Article Abstract will be a WordCloud created by ML – one image per article

Example

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?  <<<<<<<<< Article Title

Author: Larry H. Bernstein, MD, FCAP  <<<<<<<<< Author’s Name

https://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/   <<<<<<< URL

  • The system provides: “Related” what you named associated, see below  will need to be placed in the article description
  • The system provides: “Posted in” – meaning  ALL the categories of research checked off by the author that this article belong to by the SUBJECT MATTER of the article

EXAMPLE for Related” what you named associated

Related

What can we expect of tumor therapeutic response?

In “Biological Networks, Gene Regulation and Evolution”

WordCloud Visualization of LPBI’s Top Twelve Articles by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

In “Academic Publishing”

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

In “Biological Networks, Gene Regulation and Evolution”

Examples for >>>>>>>> Category of Research  live links listing in parenthesis number of articles in one category

Posted in Biological NetworksCANCER BIOLOGY & Innovations in Cancer TherapyCell BiologyDisease BiologyGenome BiologyImaging-based Cancer Patient ManagementInternational Global Work in PharmaceuticalLiver & Digestive Diseases ResearchMetabolomicsMolecular Genetics & PharmaceuticalNutritionPharmaceutical Industry Competitive IntelligencePharmaceutical R&D InvestmentPopulation Health ManagementProteomicsStem Cells for Regenerative MedicineTechnology Transfer: Biotech and Pharmaceutical | Tagged Adenosine triphosphateATPGlycolysisHypoxia-inducible factorsKrebLactate dehydrogenaseMammalian target of rapamycinMitochondrionWarburg Effect | 40 Comments

Below, an excerpt from the 6,000 LIST: Top Posts by VIEWS for all days ending 2020-06-02 (Summarized)

 

All Time      
Title Views Author Name Type of Article
Home page / Archives 676,690 Internet Access Tabulation
Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View? 17,117 Larry H. Bernstein, MD, FACP Investigator Initiated Research
Recent comprehensive review on the role of ultrasound in breast cancer management 14,242 Dr. D. Nir Commission by Aviva Lev-Ari, PhD, RN
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran) 13,839 Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN Commission by Aviva Lev-Ari, PhD, RN
Paclitaxel vs Abraxane (albumin-bound paclitaxel) 13,709 Tilda Barliya, PhD Investigator Initiated Research
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications 8,230 Aviva Lev-Ari, PhD, RN Investigator Initiated Research
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care 7,903 Dr. Pearlman, MD, PhD, FACC & Aviva Lev-Ari, PhD, RN Investigator Initiated Research
Mesothelin: An early detection biomarker for cancer (By Jack Andraka) 6,540 Tilda Barliya, PhD Investigator Initiated Research
Our TEAM 6,505 Internet Access Tabulation
Biochemistry of the Coagulation Cascade and Platelet Aggregation: Nitric Oxide: Platelets, Circulatory Disorders, and Coagulation Effects 5,221 Larry H. Bernstein, MD, FACP Investigator Initiated Research
Interaction of enzymes and hormones 4,901 Larry H. Bernstein, MD, FACP Commission by Aviva Lev-Ari, PhD, RN
Akt inhibition for cancer treatment, where do we stand today? 4,852 Ziv Raviv, PhD Investigator Initiated Research
AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation 4,535 Stephen J. Williams, PhD Investigator Initiated Research
The History and Creators of Total Parenteral Nutrition 4,511 Larry H. Bernstein, MD, FACP Commission by Aviva Lev-Ari, PhD, RN
Newer Treatments for Depression: Monoamine, Neurotrophic Factor & Pharmacokinetic Hypotheses 4,365 Zohi Sternberg, PhD Investigator Initiated Research
FDA Guidelines For Developmental and Reproductive Toxicology (DART) Studies for Small Molecules 4,188 Stephen J. Williams, PhD Investigator Initiated Research
The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets 4,038 Dr. Pearlman, MD, PhD, FACC, Larry H. Bernstein, MD, FACP & Aviva Lev-Ari, PhD, RN Commission by Aviva Lev-Ari, PhD, RN
Founder 3,895 Aviva Lev-Ari, PhD, RN Investigator Initiated Research

EndFragment

A.2 A List of 16 e-BOOKS

https://lnkd.in/ekWGNqA

 

A.2.1   Each book is made of articles included in the N=6,000

A.2.2 Books will list the URL of each book

http://www.amazon.com/dp/B00DINFFYC

http://www.amazon.com/dp/B018Q5MCN8

http://www.amazon.com/dp/B018PNHJ84

http://www.amazon.com/dp/B018DHBUO6

http://www.amazon.com/dp/B013RVYR2K

http://www.amazon.com/dp/B012BB0ZF0

http://www.amazon.com/dp/B019UM909A

http://www.amazon.com/dp/B019VH97LU

http://www.amazon.com/dp/B071VQ6YYK

https://www.amazon.com/dp/B075CXHY1B

https://www.amazon.com/dp/B076HGB6MZ

https://www.amazon.com/dp/B078313281

https://www.amazon.com/dp/B078QVDV2W

https://www.amazon.com/dp/B07MGSFDWR

https://www.amazon.com/dp/B07MKHDBHF

https://www.amazon.com/dp/B08385KF87

A.3 A List of e-Proceedings and Tweet Collections

A.3.1 each entry is an article included in N=6,000

B.   2.0 LPBI – 2021–2025 –

IP Assets under construction –

WILL BE AVAILABLE FOR SALE

 

B.1 Journal articles

  • Will be subjected to ML and a NEW product will be created
  • Instead of N=6,000 article – we will have N= 6,000 Medical INSIGHTS

B.2 16 e-Books

  • Will be subjected to ML and a NEW product will be created
  • Instead of 16 Books – we will have 16 COLLECTIONS of Medical INSIGHTS derived from Text Analysis of ONLY the articles included on each Volume
  • 16 e-Books will become 16 AUDIO BOOKS
  • 16 e-Books will become 16 Books in Japanese, Spanish and Russians

 

B.3 eProceedings & Tweet collections

  • Will be subjected to ML and a NEW product will be created
  • Instead of 60 e-Proceedings and 30 Tweet collections we will get 100 Business INSIGHTS Collections in the domain of each conference

 

We believe that Blockchain will enable STORAGE of each item that will be available for sale

  • LPBI will have team members Bundling items per customer needs 
  • Promotion can be done OUTSIDE the Blockchain system – STIRRING Customers to the Blockchain transaction system for TRADE and recording of transactions
  • That is true for A and for B, below

A.   1.0 LPBI – 2012–2020 – IP Assets available for sale

B.   2.0 LPBI – 2021–2025 – IP Assets under construction – WILL BE AVAILABLE FOR SALE

 

Data Architecture Questions

 

  1. In what data format is the content stored? In other words, is the content in image pdfs, searchable document pdfs, html, xls, word documents, text files, or some other form?

Example: TEXT

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

My proposed Elevator Pitch

For the first time in the ten years of our private ownership, the opportunity to acquire the Inventor of Scientific curation has become a reality, Available for Transfer of ownership.

You can own a portfolio of Intellectual Property Assets that commands ~2MM e-Readers and offers ~6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. Pages of our 16 books have been downloaded ~125,000 times and over 100 of the top biotech and medical conferences were covered in real time and recorded in writing and Tweets. New strategies in AI and Blockchain are now applied on LPBI’s content for INSIGHT searches and pattern recognition by automated Machine Learning algorithms for use in drug discovery and drug repurposing. All of LPBI’s content was created by our Experts, Authors, Writers (EAWs).

    • We UPLOAD MS Word file NOT PDF
    • INVENTORY is stored in Excel
    • Top Posts for all days ending 2020-11-16 (Summarized)
      1. 7 Days |30 Days |Quarter |Year |All time
    • All Time
  • Title
  • Views
  • 716,030
  • 17,263
  • 15,300
  • 14,341
  • 14,006
  • 8,770
  • 8,398
  • 6,632
  • 6,580
  • 5,536
  • 5,304
  • 5,056
  • 4,899
  • 4,712
  • 4,665
  • 4,453
  • 4,416
  • 4,335
  • 4,206
  • 4,126
  • 4,118
  1. Within each content file or dataset, is the content metadata already defined, or would we need to parse the file to pull out the metadata? In other words, in the file for a journal article, do you already have the author, date, abstract, keywords, etc. defined as discrete pieces of data, or is all of this information embedded within the overall file?

YES

They need to be Indexed by several keys:

A.1.1  Author Name

A.1.2  Article Title

A.1.3  Category of Research

 

  1. Do you expect to use a single type of subscription (such as a monthly subscription), or will different types of data have different types 

of subscription options (similar to how journals offer both one-time 24-hour subscriptions to a single article as well as monthly ongoing subscriptions)?

We wish to SELL ARTICLE DOWNLOAD vs Subscriptions

  1. Does the marketplace need to include fuzzy search (i.e., the ability to find content based on “similar to” criteria, instead of just exact match searches)? Does it need to present the user with related content, or only the content that was searched for?

Our system attaches to each article RELATED content

  1. We assume that the marketplace is not intended to replace your current LPBI company website? We are not scoping the quote to include a full website rebuild; it is assumed that the marketplace is separate (and your users would access the marketplace via the LPBI website).

YES – the digital store will connect to our newly to be designed web site for 2.0 LPBI on WordPress.com

  • The digital store is the FORUM to buy goods by digital download of content
  • $30 for One digital article or Audio article
  • REFERRAL to Amazon Website to buy a book or the book in AUDIO format or a book in Japanese and Spanish – Russia is not served by Amazon – we can sell directly to consumers
  • $100 download of an e-Proceedings for a Conference or the Tweet collection

For 2.0 LPBI Products

Bundles of Insights for Targeted Industries – B–to-B

  • Tier #1:  Insights for drug discovery embedded in consulting engagements
  • Tier #2:  Insights for drug repurposing embedded in consulting engagements
  • Tier #3:  Insights for Health Care Insurers embedded in consulting engagements

Bundles of insights for theScientific Community – B–to-C

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The Nobel Prize in Chemistry 2020: Emmanuelle Charpentier & Jennifer A. Doudna

Reporters: Stephen J. Williams, Ph.D. and Aviva Lev-Ari, PhD, RN

 

UPDATED on 11/12/2020

Harvard’s Jack Szostak congratulates former advisee Jennifer Doudna

It was a toast from one Nobel laureate to another, sweetened by the pride of a mentor to a prized student.

When Jennifer Doudna Ph.D. ’89 was honored on Wednesday with the Nobel Prize in chemistry for her work on the CRISPR gene-editing technique, she became the second person to gain such an honor from the lab of Jack Szostak, a genetics professor at Harvard Medical School and Massachusetts General Hospital, and professor of chemistry and chemical biology at Harvard’s Faculty of Arts and Sciences.

Szostak, who won the Nobel Prize in physiology or medicine in 2009 for work on how telomere caps keep the body’s chromosomes from breaking down, advised Doudna’s doctoral work on RNA and on Wednesday raised a glass in honor of Doudna, now at the University of California, Berkeley. In a tweet, Szostak expressed his delight at seeing someone he once guided through her early scientific steps soar to science’s highest reaches:

Doudna received the prize together with Emmanuelle Charpentier, for their work discovering and developing CRISPR as a precise gene-editing tool. In just the eight years since the pair announced their discovery the use of the technique has rapidly spread to a host of fields, allowing researchers to alter the code of life and develop resistant crops, new medical therapies, and even anticipate curing inherited diseases.

 

UPDADTED on 11/2/2020

 

Announcement of the Nobel Prize in Chemistry 2020

Live webcast from the press conference where the Royal Swedish Academy of Sciences will announce the Nobel Prize in Chemistry 2020.

 

 

The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2020 to

Emmanuelle Charpentier
Max Planck Unit for the Science of Pathogens, Berlin, Germany

Jennifer A. Doudna
University of California, Berkeley, USA

“for the development of a method for genome editing”

Genetic scissors: a tool for rewriting the code of life

Emmanuelle Charpentier and Jennifer A. Doudna have discovered one of gene technology’s sharpest tools: the CRISPR/Cas9 genetic scissors. Using these, researchers can change the DNA of animals, plants and microorganisms with extremely high precision. This technology has had a revolutionary impact on the life sciences, is contributing to new cancer therapies and may make the dream of curing inherited diseases come true.

Researchers need to modify genes in cells if they are to find out about life’s inner workings. This used to be time-consuming, difficult and sometimes impossible work. Using the CRISPR/Cas9 genetic scissors, it is now possible to change the code of life over the course of a few weeks.

“There is enormous power in this genetic tool, which affects us all. It has not only revolutionised basic science, but also resulted in innovative crops and will lead to ground-breaking new medical treatments,” says Claes Gustafsson, chair of the Nobel Committee for Chemistry.

As so often in science, the discovery of these genetic scissors was unexpected. During Emmanuelle Charpentier’s studies of Streptococcus pyogenes, one of the bacteria that cause the most harm to humanity, she discovered a previously unknown molecule, tracrRNA. Her work showed that tracrRNA is part of bacteria’s ancient immune system, CRISPR/Cas, that disarms viruses by cleaving their DNA.

Charpentier published her discovery in 2011. The same year, she initiated a collaboration with Jennifer Doudna, an experienced biochemist with vast knowledge of RNA. Together, they succeeded in recreating the bacteria’s genetic scissors in a test tube and simplifying the scissors’ molecular components so they were easier to use.

In an epoch-making experiment, they then reprogrammed the genetic scissors. In their natural form, the scissors recognise DNA from viruses, but Charpentier and Doudna proved that they could be controlled so that they can cut any DNA molecule at a predetermined site. Where the DNA is cut it is then easy to rewrite the code of life.

Since Charpentier and Doudna discovered the CRISPR/Cas9 genetic scissors in 2012 their use has exploded. This tool has contributed to many important discoveries in basic research, and plant researchers have been able to develop crops that withstand mould, pests and drought. In medicine, clinical trials of new cancer therapies are underway, and the dream of being able to cure inherited diseases is about to come true. These genetic scissors have taken the life sciences into a new epoch and, in many ways, are bringing the greatest benefit to humankind.

Illustrations

The illustrations are free to use for non-commercial purposes. Attribute ”© Johan Jarnestad/The Royal Swedish Academy of Sciences”

Illustration: Using the genetic scissors (pdf)
Illustration: Streptococcus’ natural immune system against viruses:CRISPR/Cas9 pdf)
Illustration: CRISPR/Cas9 genetic scissors (pdf)

Read more about this year’s prize

Popular information: Genetic scissors: a tool for rewriting the code of life (pdf)
Scientific Background: A tool for genome editing (pdf)

Emmanuelle Charpentier, born 1968 in Juvisy-sur-Orge, France. Ph.D. 1995 from Institut Pasteur, Paris, France. Director of the Max Planck Unit for the Science of Pathogens, Berlin, Germany.

Jennifer A. Doudna, born 1964 in Washington, D.C, USA. Ph.D. 1989 from Harvard Medical School, Boston, USA. Professor at the University of California, Berkeley, USA and Investigator, Howard Hughes Medical Institute.

SOURCE

https://www.nobelprize.org/prizes/chemistry/2020/press-release/

 

Nobel Prize in Chemistry awarded to scientists who discovered CRISPR gene editing tool for ‘rewriting the code of life’

(CNN)The Nobel Prize in Chemistry has been awarded to Emmanuelle Charpentier and Jennifer A. Doudna for the development of a method for genome editing.

They discovered one of gene technology’s sharpest tools: the CRISPR/Cas9 genetic scissors. Using these, researchers can change the DNA of animals, plants and micro-organisms with extremely high precision.
Before announcing the winners on Wednesday, Göran K. Hansson, secretary-general for the Royal Swedish Academy of Sciences, said that this year’s prize was about “rewriting the code of life.”
The American biochemist Jennifer A. Doudna (left) and French microbiologist Emmanuelle Charpentier, pictured together in 2016.
 
The CRISPR/Cas9 gene editing tools have revolutionized the molecular life sciences, brought new opportunities for plant breeding, are contributing to innovative cancer therapies and may make the dream of curing inherited diseases come true, according to a press release from the Nobel committee.
 
 
There have also been some ethical concerns around the CRISPR technology, however.
Charpentier, a French microbiologist, and Doudna, an American biochemist, are the first women to jointly win the Nobel Prize in Chemistry, and the sixth and seventh women to win the chemistry prize.
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Jennifer Doudna wins 2020 Nobel Prize in chemistry

 

First Day in a Nobel Life: Jennifer Doudna

12,365 views
Oct 7, 2020
 
Scenes from day that UC Berkeley Professor Jennifer Doudna won the Nobel Prize For the full story, visit: https://news.berkeley.edu/2020/10/07/… University of California, Berkeley, biochemist Jennifer Doudna today won the 2020 Nobel Prize in Chemistry, sharing it with colleague Emmanuelle Charpentier for the co-development of CRISPR-Cas9, a genome editing breakthrough that has revolutionized biomedicine. CRISPR-Cas9 allows scientists to rewrite DNA — the code of life — in any organism, including human cells, with unprecedented efficiency and precision. The groundbreaking power and versatility of CRISPR-Cas9 has opened up new and wide-ranging possibilities across biology, agriculture and medicine, including the treatment of thousands of intractable diseases. Doudna and Charpentier, director of the Max Planck Institute for Infection Biology, will share the 10 million Swedish krona (more than $1 million) prize. “This great honor recognizes the history of CRISPR and the collaborative story of harnessing it into a profoundly powerful engineering technology that gives new hope and possibility to our society,” said Doudna. “What started as a curiosity‐driven, fundamental discovery project has now become the breakthrough strategy used by countless researchers working to help improve the human condition. I encourage continued support of fundamental science as well as public discourse about the ethical uses and responsible regulation of CRISPR technology.” Video by Clare Major & Roxanne Makasdjian
SOURCE

 

Jennifer Doudna wins 2020 Nobel Prize in chemistry

 

Jennifer Doudna in the PBS Movie CRISPR

Our critically-acclaimed documentary HUMAN NATURE is now streaming on NETFLIX. #HumanNatureFilm. Find out more about the film on our website.

 

Other Articles on the Nobel Prize in this Open Access Journal Include:

2020 Nobel Prize for Physiology and Medicine for Hepatitis C Discovery goes to British scientist Michael Houghton and US researchers Harvey Alter and Charles Rice

CONTAGIOUS – About Viruses, Pandemics and Nobel Prizes at the Nobel Prize Museum, Stockholm, Sweden 

AACR Congratulates Dr. William G. Kaelin Jr., Sir Peter J. Ratcliffe, and Dr. Gregg L. Semenza on 2019 Nobel Prize in Physiology or Medicine

2018 Nobel Prize in Physiology or Medicine for contributions to Cancer Immunotherapy to James P. Allison, Ph.D., of the University of Texas, M.D. Anderson Cancer Center, Houston, Texas. Dr. Allison shares the prize with Tasuku Honjo, M.D., Ph.D., of Kyoto University Institute, Japan

2017 Nobel prize in chemistry given to Jacques Dubochet, Joachim Frank, and Richard Henderson  for developing cryo-electron microscopy

2016 Nobel Prize in Chemistry awarded for development of molecular machines, the world’s smallest mechanical devices, the winners: Jean-Pierre Sauvage, J. Fraser Stoddart and Bernard L. Feringa

Correspondence on Leadership in Genomics and other Gene Curations: Dr. Williams with Dr. Lev-Ari

Programming life: An interview with Jennifer Doudna by Michael Chui, a partner of the McKinsey Global Institute

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