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How AI Can Transform Pharmacovigilance And Monitor Safety Of Medicinal Products

#artificialintelligence

Nicole Baker who is the co-founder of biologit, an early-stage technology startup using artificial intelligence solutions for pharmacovigilance and clinical safety, took us through an interesting session on why AI is needed for monitoring safety of medicinal products at Rising 2020. Baker who started as an immunologist soon realised that there is a lot of data to read through and that's when she explored the use of artificial intelligence to bring about ease and efficiency in her pharmacovigilance work. For the uninitiated, pharmacovigilance is the science and activities relating to detection, assessment, understanding and prevention of adverse effects or any other medicine-related problems. Before a medicine is authorised for use, evidence of its safety and efficacy is limited to the results from clinical trials, but after the medicine goes into public use, there can still be cases of adverse drug reactions which can be reported by doctors, nurses, and even users themselves. Pharmacovigilance involves ensuring that the patient is safe and that the medicine is not causing adverse reactions.


China and AI: What the World Can Learn and What It Should Be Wary of

#artificialintelligence

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars. The move has led--at least in the West--to warnings of a global AI arms race and concerns about the growing reach of China's authoritarian surveillance state. But treating China as a "villain" in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government's approach to AI that are highly concerning and rightly should be condemned, it's important that this does not cloud all analysis of China's AI innovation.


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]: 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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.