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Pharmaceuticals & Biotechnology


AI is reinventing the way we invent

#artificialintelligence

Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.


Stock Scanner Based on Genetic Algorithms: Returns up to 486.89% in 3 Months

#artificialintelligence

This stock scanner is part of the Risk-Conscious Package, as one of I Know First's equity research solutions. We determine our aggressive stock picks by screening our algorithm daily for higher volatility stocks that present greater opportunities but are also riskier. Package Name: Aggressive Stocks Forecast Recommended Positions: Long Forecast Length: 3 Months (4/1/2020 – 7/1/2020) I Know First Average: 100.66% The highest trade return came from NVAX, at 486.89%. NLS and DPW followed with returns of 259.77% and 213.26% for the 3 Months period.


Tracing cell trajectories in a biofilm

Science

Born in 1881 on a farm in Pennsylvania, Alice C. Evans dedicated her life to studying bacteria in dairy products. Early in her career, Alice became convinced that most bacteria display multicellular behavior as part of their life cycles. At the time, the morphological changes observed in bacterial life cycles created confusion among scientists. In 1928, as the first female president of the American Society for Microbiology, Alice wrote to the scientific community: “When one-celled organisms grow in masses, … individual cells influence and protect one another.” She continued, “Bacteriologists need not feel chagrinned … to admit that… forms they have considered as different genera are but stages in the life cycle of one species” ([ 1 ][1]). Nearly 100 years later, on page 71 of this issue, Qin et al. ([ 2 ][2]) make a substantial leap forward in deciphering cell dynamics in biofilms—groups of microorganisms that adhere to a surface, and each other, by excreting matrix components. In the interim period, microbiologists have learned that many bacteria organize in groups. This allows bacterial cells to achieve collectively what individuals in isolation cannot, thus conferring a selective advantage on the individuals. Multicellular behaviors help cells to migrate ([ 3 ][3]), resist antibiotic treatments ([ 4 ][4]), and protect themselves from predators ([ 5 ][5]). In recent years, microbiologists have begun to unravel the mechanisms behind these multicellular behaviors, by studying single-cell gene expression, growth rate regulation, and cell-to-cell interactions ([ 6 ][6]–[ 9 ][7]), as well as by developing tools to investigate the morphology and growth of entire bacterial biofilms ([ 10 ][8], [ 11 ][9]). A multicellular aggregate starts with a single founder cell that grows into a mature biofilm. Despite substantial progress, scientists still lack a detailed understanding of how bacterial cells are programmed to build multicellular structures. Each cell makes individual decisions—whether to divide, move, excrete chemicals, exert forces, or express extracellular matrix components—in response to its local environment. In turn, the local environment is determined by the collective decisions of all of its cells, played out as a mosaic over time in a three-dimensional (3D) space. A primary challenge to unraveling the mystery of how cells are programmed to produce a mature functional biofilm is that researchers lack the experimental tools needed to study how the dynamics of individual cells drive biofilm formation and structure. ![Figure][10] The building of biofilms A fountain-like flow of bacterial cells drives biofilm expansion. CREDIT: V. ALTOUNIAN/ SCIENCE In their elegant study, Qin et al. developed dual-view light-sheet microscopy to reconstruct single-cell trajectories in 3D Vibrio cholerae biofilms initiated by a single founder cell. This method fluorescently labeled cellular puncta, giving isotropic single-cell resolution in the biofilm with much less photobleaching than that seen with previous methods. This advance allowed the authors to carry out simultaneous imaging of 10,000 V. cholerae cells for the 16 hours it takes for the biofilm to develop, with 3-min intervals between subsequent images. This frequent imaging made it possible to track the trajectories of micrometer-sized cells, giving an unprecedented view into the behaviors of individual cells as the biofilm developed (see the figure). The measurements revealed a qualitative transition in an individual cell's behavior, in which Brownian motion changes to ballistic motion as the biofilm develops. This transition corresponds to a new phase of collective growth, when the biofilm as a whole begins its vertical expansion away from the substrate. In this phase, cells displayed two types of trajectories. Some of the cells expanded ballistically outward, whereas others became trapped at the substrate. Overall, these trajectories gave rise to a collective fountain-like flow, which transported some cells to the biofilm front, while bypassing the cells trapped at the substrate. This fountain-like flow allowed for fast lateral expansion of the biofilm. Cell tracking allowed Qin et al. to precisely quantify the dynamics of various cells, while also assessing how these dynamics differ for mutant cells that overproduce matrix components. To interpret the results, the authors built a mathematical model for the mechanics of biofilm expansion, balancing growth with substrate friction. By modeling different surface frictions and comparing the predicted cell motion with the observed cell motion, Qin et al. were able to explain the observed behavior as long as friction between the cells and surface was a dominant effect. This study of V. cholerae offers an exciting insight into how collective behavior can arise from processes operating at the single-cell level. The mechanisms uncovered with a gram-negative bacterial species likely will be generalizable across other bacterial types. For example, the qualitative transitions in biofilm expansion observed in this study have analogs in other bacterial biofilms. With the gram-positive bacterium Bacillus subtilis , a qualitative change in colony expansion is triggered by a cellular bistable switch in which cells expressing flagella produce extracellular matrices ([ 12 ][11], [ 13 ][12]). Osmolarity associated with matrix production drives colony expansion ([ 14 ][13]). More broadly, this study demonstrates the great potential for advances in imaging technology and computer vision to help unravel how collective behavior arises from the activity of individual cells and their interactions. However, there is much more going on inside a biofilm that cannot yet be seen. More complete information would allow researchers to not only reconstruct the motion of cells but also uncover their phenotypic states. Previous work on B. subtilis with fluorescent labeling of genetic components shows detailed spatial arrangement of various cell types, with cells carrying out different biological functions in distinct parts of the biofilm ([ 3 ][3], [ 15 ][14]). One can only hypothesize about the diversity of cellular types and functions inside the beautiful fountain revealed in the present study. A deeper understanding of bacterial multicellular behavior will increase our ability to treat bacterial infections, control natural bacterial communities, and engineer synthetic ones for specific purposes. 1. [↵][15]1. A. C. Evans , J. Bacteriol. 17, 63 (1929). [OpenUrl][16][FREE Full Text][17] 2. [↵][18]1. B. Qin et al ., Science 369, 71 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. J. van Gestel et al ., PLOS Biol. 13, e1002141 (2015). [OpenUrl][22][CrossRef][23][PubMed][24] 4. [↵][25]1. C. W. Hall, 2. T.-F. Mah , FEMS Microbiol. Rev. 41, 276 (2017). [OpenUrl][26][CrossRef][27] 5. [↵][28]1. P. K. Raghupathi et al ., Front. Microbiol. 8, 2649 (2018). [OpenUrl][29] 6. [↵][30]1. A. Dal Co, 2. S. van Vliet, 3. M. Ackermann , Philos. Trans. R. Soc. London Ser. B 374, 20190080 (2019). [OpenUrl][31] 7. 1. A. Dal Co et al ., Nat. Ecol. Evol. 4, 366 (2020). [OpenUrl][32] 8. 1. S. van Vliet et al ., Cell Syst. 6, 496 (2018). [OpenUrl][33] 9. [↵][34]1. A. Dragoš et al ., Curr. Biol. 28, 1903 (2018). [OpenUrl][35][CrossRef][36] 10. [↵][37]1. K. Drescher et al ., Proc. Natl. Acad. Sci. U.S.A. 113, E2066 (2016). [OpenUrl][38][Abstract/FREE Full Text][39] 11. [↵][40]1. R. Hartmann et al ., Nat. Phys. 15, 251 (2019). [OpenUrl][41][CrossRef][42][PubMed][43] 12. [↵][44]1. H. Vlamakis et al ., Chemtracts 20, 427 (2007). [OpenUrl][45] 13. [↵][46]1. D. B. Kearns et al ., Mol. Microbiol. 55, 739 (2005). [OpenUrl][47][CrossRef][48][PubMed][49][Web of Science][50] 14. [↵][51]1. A. Seminara et al ., Proc. Natl. Acad. Sci. U.S.A. 109, 1116 (2012). [OpenUrl][52][Abstract/FREE Full Text][53] 15. [↵][54]1. H. Vlamakis et al ., Nat. Rev. Microbiol. 11, 157 (2013). [OpenUrl][55][CrossRef][56][PubMed][57] Acknowledgments: A.D.C. and M.P.B. are supported by the National Science Foundation (DMS-1715477), Materials Research Science and Engineering Center (DMR-1420570), the Office of Naval Research (N00014-17-1-3029), and the Simons Foundation. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-9 [8]: #ref-10 [9]: #ref-11 [10]: pending:yes [11]: #ref-12 [12]: #ref-13 [13]: #ref-14 [14]: #ref-15 [15]: #xref-ref-1-1 "View reference 1 in text" [16]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2BBacteriology%26rft.stitle%253DJ.%2BBacteriol.%26rft.aulast%253DEvans%26rft.auinit1%253DA.%2BC.%26rft.volume%253D17%26rft.issue%253D2%26rft.spage%253D63%26rft.epage%253D77%26rft.atitle%253DLIFE%2BCYCLES%2BIN%2BBACTERIA.%26rft_id%253Dinfo%253Apmid%252F16559356%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6MzoiUERGIjtzOjExOiJqb3VybmFsQ29kZSI7czoyOiJqYiI7czo1OiJyZXNpZCI7czo3OiIxNy8yLzYzIjtzOjQ6ImF0b20iO3M6MjE6Ii9zY2kvMzY5LzY0OTkvMzAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DQin%26rft.auinit1%253DB.%26rft.volume%253D369%26rft.issue%253D6499%26rft.spage%253D71%26rft.epage%253D77%26rft.atitle%253DCell%2Bposition%2Bfates%2Band%2Bcollective%2Bfountain%2Bflow%2Bin%2Bbacterial%2Bbiofilms%2Brevealed%2Bby%2Blight-sheet%2Bmicroscopy%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abb8501%26rft_id%253Dinfo%253Apmid%252F32527924%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: 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/lookup/external-ref?access_num=10.1111/j.1365-2958.2004.04440.x&link_type=DOI [49]: /lookup/external-ref?access_num=15661000&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom [50]: /lookup/external-ref?access_num=000226457800008&link_type=ISI [51]: #xref-ref-14-1 "View reference 14 in text" [52]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.1109261108%26rft_id%253Dinfo%253Apmid%252F22232655%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [53]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMDoiMTA5LzQvMTExNiI7czo0OiJhdG9tIjtzOjIxOiIvc2NpLzM2OS82NDk5LzMwLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [54]: #xref-ref-15-1 "View reference 15 in text" [55]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BMicrobiol.%26rft.volume%253D11%26rft.spage%253D157%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrmicro2960%26rft_id%253Dinfo%253Apmid%252F23353768%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [56]: /lookup/external-ref?access_num=10.1038/nrmicro2960&link_type=DOI [57]: /lookup/external-ref?access_num=23353768&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom


AI Hype and Radiology: A Plea for Realism and Accuracy

#artificialintelligence

This opinion piece is inspired by the old Danish proverb: "Making predictions is hard, especially about the future" (1). As every reader knows, the momentum of artificial intelligence (AI) and the eventual implementation of deep learning models seem assured. Some pundits have gone considerably further, however, and predicted a sweeping AI takeover of radiology. Although many radiologists support AI and believe it will enable greater efficiency, a recent study of medical students found very different reactions (2). While such doomsday predictions are understandably attention-grabbing, they are highly unlikely, at least in the short term.


9 emerging job roles for the future of AI

#artificialintelligence

AI is poised to transform nearly every industry, and with it will come significant changes for many job functions. Alongside this transformation of how many IT and business staff do their work will be the emergence of new jobs targeted at making the most of organizational AI strategies. Machine learning engineers have already cemented their place as must-have members of AI teams, taking first place in Indeed's best jobs list last year. AI specialist was also the top job in LinkedIn's 2020 Emerging Jobs report, with 74 percent annual growth in the last four years, followed by robots engineer and data scientist. Get the latest insights with our CIO Daily newsletter.


'DeepSqueak' A.I. Decodes Mice Chatter

#artificialintelligence

In what is somehow the cutest science story of the new year so far, scientists at the University of Washington have announced a new artificial intelligence system for decoding mouse squeaks. Dubbed DeepSqueak, the software program can analyze rodent vocalizations and then pattern-match the audio to behaviors observed in laboratory settings. As such, the software can be used to partially decode the language of mice and other rodents. Researchers hope that the technology will be helpful in developing a broad range of medical and psychological studies. Published this week in the journal Neuropsychopharmacology, the study is based around a novel use of sonogram technology, which transforms an audio signal into an image or series of graphs.


Regain Power - OKRA

#artificialintelligence

For far too long, sales reps and commercial managers in the pharmaceutical industry have had their responsibilities eroded by wave after wave of IT implementations, each one supposedly making life easier - but doing the exact opposite. The time has come for a more intelligent solution. It's time for technology to empower, not overrule. It's time for sales and marketing managers to regain control. Single black-box'next best actions' which dictate and disempower If you want to re-empower your sales and marketing staff with a smarter generation of technology, enter your details.


AI For All: The US Introduces New Bill For Affordable Research

#artificialintelligence

Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all. The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.


50 Machine Learning and Data Science Companies That Are Revolutionizing Industries

#artificialintelligence

Nowadays it's hard to find a single industry where machine learning and data science aren't being used to improve productivity and deliver results. Indeed that is why people are so excited about the promise of artificial intelligence: it can be applied to so many diverse problem spaces effectively and it works! This list has been aggregated after analyzing over 200 company descriptions, and we've broadly organized them by the problem domain being tackled and have included a brief description of their mission. TLDR: A framework for providing data integrations and web interfaces for trained machine learning models. TLDR: Develops medical imaging tools powered by AI to help improve the efficacy of radiologists in detecting illnesses.


Asia-Pacific Artificial Intelligence (AI) in Drug Discovery Market 2020-2026 by Offering, Technology, Drug Type, Therapeutic Area, Application, End-user and Country - ResearchAndMarkets.com

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DUBLIN--(BUSINESS WIRE)--The "Asia-Pacific Artificial Intelligence (AI) in Drug Discovery Market 2020-2026 by Offering, Technology, Drug Type, Therapeutic Area, Application, End User, and Country: Trend Forecast and Growth Opportunity" report has been added to ResearchAndMarkets.com's offering. Asia-Pacific artificial intelligence (AI) in drug discovery market will grow by 33.2% over 2020-2026 with a total addressable market cap of $2.78 billion, owing to fast adoption of AI technology in pharmaceutical industry and drug development. The report provides historical market data for 2015-2019, revenue estimates for 2020, and forecasts from 2021 till 2026. Highlighted with 34 tables and 53 figures, this 121-page report is based on a comprehensive research of the entire Asia Pacific AI in drug discovery market and all its sub-segments through extensively detailed classifications. Profound analysis and assessment are generated from premium primary and secondary information sources with inputs derived from industry professionals across the value chain.