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This Is One Of The Greatest Secular Growth Trends

#artificialintelligence

There are many terms that could describe the various facets of the secular growth trend of data analytics and artificial intelligence, and there also are different shades of definitions for those terms. For the purposes of this research note, I will lay out how I define each descriptive category. I specifically chose the order in which I'm sharing these terms because I view them as graduations from the base level to the most advanced level, i.e., real, human-like intelligence, or AI. This is, of course, just one lens through which we can view this industry. So with these ideas in mind, let's explore how data science, machine learning, and artificial intelligence have been rising to prominence in the business community. What's very interesting to me is that AI seems to be eating the world, and there are companies sprouting up all over the place focused on leveraging AI to enhance business or health outcomes. Livongo (LVGO) is just one such AI-based healthcare company on which I've been focused throughout 2020, though there are literally countless at this stage.


FDA Clears First-in-World Hematology App, Unlocking Potential of Diagnosis

#artificialintelligence

Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was granted FDA clearance to market and sell its X100 with Full Field Peripheral Blood Smear (Full Field PBS) Application, unlocking the potential of in vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year. Blood is one of the most foundational gateways to health information. Even with the adoption of digital tools, today's solutions do not showcase all required regions of interest in a PBS slide, only capturing snapshots of cells. To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools.


AI can analyze 'rash selfies' to diagnose Lyme disease

#artificialintelligence

Artificial intelligence can be used to evaluate smartphone photos of suspicious rashes and detect Lyme disease earlier, according to a new study. Lyme disease affects roughly 300,000 people in the US every year and is transmitted through the bite of an infected deer tick. A painless rash, called Erythema migrans (EM), usually appears a week or so later, followed by more serious symptoms including fever, headache, chills, joint pain and swollen lymph glands. Lyme disease is most effectively treated if caught early. Untreated, it can cause cognitive impairment, chronic fatigue, heart palpitations and painful swelling that can last from months to years.


AI can analyze smartphone 'rash selfies' to diagnose Lyme disease

Daily Mail - Science & tech

Artificial intelligence can be used to evaluate smartphone photos of suspicious rashes and detect Lyme disease earlier, according to a new study. Lyme disease affects roughly 300,000 people in the US every year and is transmitted through the bite of an infected deer tick. A painless rash, called Erythema migrans (EM), usually appears a week or so later, followed by more serious symptoms including fever, headache, chills, joint pain and swollen lymph glands. Lyme disease is most effectively treated if caught early. Untreated, it can cause cognitive impairment, chronic fatigue, heart palpitations and painful swelling that can last from months to years.


AI and deep learning can analyze 'rash selfies' for better Lyme disease detection – IAM Network

#artificialintelligence

Examples of correct and incorrect visual identifications of the erythema migrans (EM) rash commonly seen in patients with Lyme disease. The images in the top right quadrant actually are EM (true positives). The upper right photos are false negatives, the lower left are false positives and the lower right were correctly ruled out as EM (true negatives). A new AI/deep learning technique from Johns Hopkins Medicine and the Johns Hopkins Applied Research Laboratory greatly increases the chances of correctly identifying EM in photographs. Johns Hopkins Medicine and Johns Hopkins Applied Research Laboratory (APL) researchers have shown that cell phone images of rashes taken by patients can be evaluated using artificial intelligence (AI) and deep learning (DL) technologies to more accurately detect and identify the erythema migrans (EM) skin redness associated with acute Lyme disease.


Research Story Tip: AI and Deep Learning Can Analyze 'Rash Selfies' for Better Lyme Disease Detection

#artificialintelligence

A report on the findings was published in the October 2020 issue of the journal Computers in Biology and Medicine. APL scientists developed and tested several deep learning computer models to accurately pick out EM from other dermatological conditions and normal skin. The DL models were "trained" to discern the appearance of EM using images of non-EM rashes and normal skin available in the public domain, and clinical photos of patients with EM provided by the Johns Hopkins University Lyme Disease Research Center and the Lyme Disease Biobank, part of the Johns Hopkins University School of Medicine's Division of Rheumatology. There are more than 300,000 new cases of Lyme disease annually in the United States and treatment is most effective if it is caught early. Misdiagnosis, especially in the disease's initial stages, is common because of several challenges.


Cerebral Organoids: Conscious Subjects or Zombies?

#artificialintelligence

In 2011, at the Institute of Molecular Biotechnology in Vienna a postdoctoral researcher, Madeline Lancaster, inadvertently brought about the production of a brain organoid from human embryonic stem cells. The brain organoids neuroscientists can now grow consist of several million neurons. Brain organoids can be produced much as other 3D multicellular structures resembling eye, gut, liver, kidney and other human tissues have been built. By adding appropriate signaling factors, aggregates of pluripotent stem cells (which have the ability to develop into any cell type) can differentiate and self-organize into structures that resemble certain regions of the human brain. There's debate about exactly how and to what extent these so-called "mini-brains" resemble human brains. Yet, given considerable similarities with respect to their constitution, neural activity, and structure, cerebral organoids can be used as reliable models of human brains, which is advantageous for neuroscientists who have limited access to the human brain as it functions.


Ethical Machine Learning in Health Care

arXiv.org Artificial Intelligence

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.


Species-specific segmentation clock periods are due to differential biochemical reaction speeds

Science

Many animals display similarities in their organization (body axis, organ systems, and so on). However, they can display vastly different life spans and thus must accommodate different developmental time scales. Two studies now compare human and mouse development (see the Perspective by Iwata and Vanderhaeghen). Matsuda et al. studied the mechanism by which the human segmentation clock displays an oscillation period of 5 to 6 hours, whereas the mouse period is 2 to 3 hours. They found that biochemical reactions, including protein degradation and delays in gene expression processes, were slower in human cells compared with their mouse counterparts. Rayon et al. looked at the developmental tempo of mouse and human embryonic stem cells as they differentiate to motor neurons in vitro. Neither the sensitivity of cells to signals nor the sequence of gene-regulatory elements could explain the differing pace of differentiation. Instead, a twofold increase in protein stability and cell cycle duration in human cells compared with mouse cells was correlated with the twofold slower rate of human differentiation. These studies show that global biochemical rates play a major role in setting the pace of development. Science , this issue p. [1450][1], p. [eaba7667][2]; see also p. [1431][3] Although mechanisms of embryonic development are similar between mice and humans, the time scale is generally slower in humans. To investigate these interspecies differences in development, we recapitulate murine and human segmentation clocks that display 2- to 3-hour and 5- to 6-hour oscillation periods, respectively. Our interspecies genome-swapping analyses indicate that the period difference is not due to sequence differences in the HES7 locus, the core gene of the segmentation clock. Instead, we demonstrate that multiple biochemical reactions of HES7 , including the degradation and expression delays, are slower in human cells than they are in mouse cells. With the measured biochemical parameters, our mathematical model accounts for the two- to threefold period difference between the species. We propose that cell-autonomous differences in biochemical reaction speeds underlie temporal differences in development between species. [1]: /lookup/doi/10.1126/science.aba7668 [2]: /lookup/doi/10.1126/science.aba7667 [3]: /lookup/doi/10.1126/science.abe0953


Tempus fugit: How time flies during development

Science

“Fugit irreparabile tempus,” wrote Virgil, a reminder that our lives are defined by the irreversible flow of time. As soon as the egg is fertilized, embryonic cells follow a developmental program strictly organized in time. The sequence typically is conserved throughout evolution, but individual events can occur over species-specific time scales. Such differences can have marked effects. For instance, it takes 3 months to generate cerebral cortex neurons in a human but only 1 week in a mouse. This prolonged neurogenesis likely contributes to evolutionary expansion of the human brain ([ 1 ][1]). But the mechanisms underlying developmental time scales remain largely unknown. On pages 1449 and 1450 of this issue, Rayon et al. ([ 2 ][2]) and Matsuda et al. ([ 3 ][3]), respectively, report an association between species-specific developmental time scales and the speed of biochemical reactions that support protein turnover. Cell differentiation during mammalian development uses two types of timing mechanisms (biological clocks) based on oscillations or unidirectional processes (hourglass clocks). Modeling development in pluripotent stem cells (PSCs) from various species shows that the pace of differentiation of many cell types in an in vitro setting largely recapitulates the species-specific timing observed in embryos ([ 4 ][4], [ 5 ][5]). Even when human neurons are transplanted as single cells in a mouse brain, they follow their own prolonged developmental timeline ([ 6 ][6]). This suggests that cell-intrinsic mechanisms, yet to be discovered, dictate the timing of developmental trajectories in a species-specific manner. Matsuda et al. examined a biological rhythm typical of vertebrate embryos: the “somite segmentation clock,” by which the body is built segment (or somite) by segment, thanks to waves of expression of specific genes (oscillations) in presomitic mesodermal (PSM) cells. Using in vitro modeling with mouse and human PSCs, the authors examined waves of expression of HES7 (hes family bHLH transcription factor 7), a segmental-clock master gene. They found similar waves in PSM cells of both species, but the period of oscillations in human cells was ∼5 hours instead of 2 hours (as in mouse cells), consistent with another recent report ([ 7 ][7]). What might underlie such cell-intrinsic differences? Evolutionary divergence in developmental processes usually occurs as a result of changes in the gene regulatory networks (GRNs) that control them ([ 8 ][8]). The authors examined the GRN of segmental oscillations, and except for the period of oscillation, they found no obvious difference between human and mouse gene expression. They then swapped the mouse and human genome sequences containing the HES7 locus. The human HES7 gene transplanted in mouse cells displayed fast oscillations like the mouse gene, whereas the mouse gene transplanted in the human cells displayed slower, human-like oscillations (see the figure). Thus, even DNA cis-regulatory components of the GRN do not appear to dictate the time scale of HES7 oscillations. However, Matsuda et al. found important species-specific differences in a different mechanism: the speed of biochemical reactions leading to protein turnover (production and decay). Human cells displayed slower kinetics of protein expression (including “expression delays” related to RNA transcription, splicing, and translation) and a slower rate of protein decay, mostly related to degradation. Many examined parameters showed a twofold difference in mouse versus human cells, matching the time differences observed for the segmentation clock. ![Figure][9] Same events, distinct timingGRAPHIC: KELLIE HOLOSKI/ SCIENCE Rather than being dominated by clocklike oscillations, the developmental process is specified mostly by cell-fate transitions, by which embryonic cells gradually an d irreversibly become differentiated cells. Could it be that similar mechanisms regulate these hourglass-like timing events as well? Rayon et al. explored this notion using a motor neuron (MN) developmental model from mouse and human PSCs. Examination of MN development in vitro revealed that the underlying GRN is similar in both species, except that human motoneurogenesis takes 2.5 times longer in the human cell model versus the mouse. The authors then examined the influence of sonic hedgehog, the key morphogen that induces MN fate (by changing timing and intensity of the signal), and the MN-development master gene OLIG2 (oligodendrocyte transcription factor 2) (by inserting the human gene in mouse cells) but found no effects that explained the species-specific time differences. They then analyzed protein stability during MN development and found that the mean protein half-life was doubled in human cells compared with mouse cells, which is consistent with the findings of Matsuda et al. Both studies point to protein turnover as a potential source of variation in developmental time scales. Each group tested this hypothesis further by in silico modeling of their experimental systems, which predicted, in each case, a prominent influence of the delay in protein production and protein decay on developmental time scales. That protein turnover affects the timing of development is provocative and attractive but must be validated by experimental evidence for causal relationship between the two (by altering the production and decay of proteins and mRNA, and then examining the developmental time scale). Such experiments will also help to determine the respective contributions of expression delay versus protein decay, on which each study puts a somewhat different emphasis. The consistent results from both studies also raise questions about the mechanisms upstream of interspecies differences in protein turnover. Metabolism is an attractive candidate. Protein turnover requires a considerable amount of energy ([ 9 ][10]), and metabolic rewiring has emerged as a central instructor of cell fate transitions ([ 10 ][11]), although through epigenetic remodeling rather than changes in proteostasis. Another question is whether the same principles apply to developmental events that display more pronounced time scale differences. For example, GRN divergence might operate through specific genes that modulate the timing of human cortical neurogenesis ([ 11 ][12]). Furthermore, metabolism and protein turnover might display differences depending on the cell context or the specific protein involved. And known correlations between developmental timing, life span, and aging across species ([ 12 ][13]) might all be causally linked to differences in metabolism and protein turnover. 1. [↵][14]1. A. M. M. Sousa et al ., Cell 170, 226 (2017). [OpenUrl][15][CrossRef][16][PubMed][17] 2. [↵][18]1. T. Rayon et al ., Science 369, eaba7667 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. M. Matsuda et al ., Science 369, 1450 (2020). [OpenUrl][22][Abstract/FREE Full Text][23] 4. [↵][24]1. J. van den Ameelen et al ., Trends Neurosci. 37, 334 (2014). [OpenUrl][25][CrossRef][26][PubMed][27] 5. [↵][28]1. M. Ebisuya, 2. J. Briscoe , Development 145, dev164368 (2018). [OpenUrl][29][Abstract/FREE Full Text][30] 6. [↵][31]1. D. Linaro et al ., Neuron 104, 972 (2019). [OpenUrl][32] 7. [↵][33]1. M. Diaz-Cuadros et al ., Nature 580, 113 (2020). [OpenUrl][34][CrossRef][35][PubMed][36] 8. [↵][37]1. E. H. Davidson, 2. D. H. Erwin , Science 311, 796 (2006). [OpenUrl][38][Abstract/FREE Full Text][39] 9. [↵][40]1. J. Labbadia, 2. R. I. Morimoto , Annu. Rev. Biochem. 84, 435 (2015). [OpenUrl][41][CrossRef][42][PubMed][43] 10. [↵][44]1. N. Shyh-Chang et al ., Development 140, 2535 (2013). [OpenUrl][45][Abstract/FREE Full Text][46] 11. [↵][47]1. I. K. Suzuki et al ., Cell 173, 1370 (2018). [OpenUrl][48][CrossRef][49][PubMed][50] 12. [↵][51]1. A. A. Fushan et al ., Aging Cell 14, 352 (2015). [OpenUrl][52][CrossRef][53][PubMed][54] Acknowledgments: P.V. is funded by the European Research Council, Belgian Fonds Wetenschappelijk Onderzoek, Excellence of Science Research programme, AXA Research Fund, Belgian Queen Elizabeth Foundation, and Fondation Université Libre de Bruxelles. R.I. was supported by the Belgian Fonds de la Recherche Scientifique. 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/lookup/external-ref?access_num=10.1111/acel.12283&link_type=DOI [54]: /lookup/external-ref?access_num=25677554&link_type=MED&atom=%2Fsci%2F369%2F6510%2F1431.atom