Computational algorithms enable identification and optimization of RNA-based tools for myriad applications — ScienceDaily

DNA and RNA have been compared to “instruction manuals” containing the information needed for living “machines” to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables — a task that is daunting to even the most intrepid human brains.

Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based “toehold” sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. As reported in two papers published concurrently

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Deep learning enables identification and optimization of RNA-based tools for myriad applications

Deep learning takes on synthetic biology
Credit: Wyss Institute at Harvard University

DNA and RNA have been compared to “instruction manuals” containing the information needed for living “machines” to operate. But while electronic machines like computers and robots are designed from the ground up to serve a specific purpose, biological organisms are governed by a much messier, more complex set of functions that lack the predictability of binary code. Inventing new solutions to biological problems requires teasing apart seemingly intractable variables—a task that is daunting to even the most intrepid human brains.


Two teams of scientists from the Wyss Institute at Harvard University and the Massachusetts Institute of Technology have devised pathways around this roadblock by going beyond human brains; they developed a set of machine learning algorithms that can analyze reams of RNA-based “toehold” sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. As reported in

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The world’s first successful identification and characterization of in vivo senescent cells

The world's first successful identification and characterization of in vivo senescent cells
The research team generate a p16-Cre ERT2 – tdTomato mouse model to uncover the in vivo dynamics and properties of p16high cells. Single-cell RNA-seq analyses of various tissues from early middle-aged p16-CreERT2-tdTomato mice reveal that p16high cells exhibit heterogenous senescence-associated phenotypes, while elimination of p16high cells ameliorates steatosis and inflammation in a NASH model. Credit: The Institute of Medical Science, The University of Tokyo

Cell senescence is a state of permanent cell cycle arrest that was initially defined for cells grown in cell culture. It plays a key role in age-associated organ dysfunction and age-related diseases such as cancer, but the in vivo pathogenesis is largely unclear.


A research team led by Professor Makoto Nakanishi of the Institute of Medical Science, the University of Tokyo, generated a p16-Cre ERT2 -tdTomato mouse model to characterize in vivo p16 high cells at the single-cell level.

They found tdTomato-positive p16 high cells detectable

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Identification of this epigenetic process highlights potential drug treatment strategies for early-stage disease treatment — ScienceDaily

New findings suggest that late-onset Alzheimer’s Disease is driven by epigenetic changes — how and when certain genes are turned on and off — in the brain. Results were published today in Nature Genetics.

Research led by Raffaella Nativio, PhD, a former research associate of Epigenetics, Shelley Berger, PhD, a professor of Genetics, Biology and Cell and Developmental Biology and Director of the Epigenetics Institute, and Nancy Bonini, PhD, a professor of Biology and Cell and Developmental Biology, all in the Perelman School of Medicine at the University of Pennsylvania, used post-mortem brain tissue to compare healthy younger and older brain cells to those with Alzheimer’s Disease. The team found evidence that epigenetic regulators disable protective pathways and enable pro-disease pathways in those with the disease.

“The last five years have seen great efforts to develop therapeutics to treat Alzheimer’s disease, but sadly, they have failed in the clinic

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Global Automated Fingerprint Identification System Markets, 2013-2018 & 2019-2024 Featuring 3M Cogent, Morpho, NEC, Crossmatch Technologies, M2SYS Technology, AFIX Technologies, Papillon Systems – ResearchAndMarkets.com

DUBLIN–(BUSINESS WIRE)–The “Automated Fingerprint Identification System Market Report: Trends, Forecast and Competitive Analysis” report has been added to ResearchAndMarkets.com’s offering.

The automated fingerprint identification system market is expected to grow with a CAGR of 21% from 2019 to 2024.

The future of the automated fingerprint identification system market looks promising with opportunities in the government, healthcare, transportation, hospitality, and banking and finance industries. The major drivers for this market are transformation and technology evolution from manual process to the digital process, increasing need for secure transaction, and increasing adoption of mobile payment solutions.

Some of the automated fingerprint identification companies profiled in this report include 3M Cogent, Morpho, NEC Corporation, Crossmatch Technologies, M2SYS Technology, AFIX Technologies, Papillon Systems

Some of the features of automated fingerprint identification Market Report: Trends, Forecast, and Opportunity Analysis include

  • Market size estimates: Automated fingerprint identification market size estimation in terms of value ($M)
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Radio Frequency Identification Market Advancements, Growth and Future Scope 2020-2026

The MarketWatch News Department was not involved in the creation of this content.

Sep 24, 2020 (Market Insight Reports) —
The report provides a detailed assessment of the ‘Global Radio Frequency Identification Market’. This includes enabling technologies, key trends, market drivers, challenges, standardization, regulatory landscape, deployment models, operator case studies, opportunities, future roadmaps, value chains, ecosystem player profiles, and strategies included. The report also presents a SWOT analysis and forecast for Radio Frequency Identification investments from 2020 to 2026.

“The Global Radio Frequency Identification Market is expected to grow at a CAGR of 7.71% during the forecast period.”

Click Here to Get Sample PDF Copy of Latest Research on Radio Frequency Identification Market 2020:

https://www.marketintelligencedata.com/reports/96679/covid-19-outbreak-global-radio-frequency-identification-industry-market-report-development-trends-threats-opportunities-and-competitive-landscape-in-2020/inquiry?source=MW&mode=87

Global Radio Frequency Identificationincludes Market Analysis Report Top Companies:AMS, STMicroelectronics (NYSE: STM), ADI, NXP, RF Solutions, Infineon, Melexis, Atmel, Microchip, Toshiba, TI have their own company profiles, growth phases, and market development opportunities.

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