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ENCODE Project’s third phase offers new insights into the organization and regulation of our genes and genome.

The Encyclopedia of DNA Elements (ENCODE) Project is a worldwide effort to understand how the human genome functions. With the completion of its latest phase, the ENCODE Project has added millions of candidate DNA “switches” from the human and mouse genomes that appear to regulate when and where genes are turned on, and a new registry that assigns a portion of these DNA switches to useful biological categories. The project also offers new visualization tools to assist in the use of ENCODE’s large datasets.

The project’s latest results were published in Nature, accompanied by 13 additional in-depth studies published in other major journals. ENCODE is funded by the National Human Genome Research Institute, part of the National Institutes of Health.

A radical new method of imaging that harnesses artificial intelligence to turn time into visions of 3D space could help cars, mobile devices and health monitors develop 360-degree awareness.

Photos and videos are usually produced by capturing photons—the building blocks of light—with digital sensors. For instance, digital cameras consist of millions of pixels that form images by detecting the intensity and color of the light at every point of space. 3D images can then be generated either by positioning two or more cameras around the subject to photograph it from multiple angles, or by using streams of photons to scan the scene and reconstruct it in three dimensions. Either way, an image is only built by gathering spatial information of the scene.

In a new paper published today in the journal Optica, researchers based in the U.K., Italy and the Netherlands describe an entirely new way to make animated 3D images: by capturing temporal information about photons instead of their spatial coordinates.

Researchers Figure Out How To Genetically Alter Squid : Shots — Health News Scientists have modified the genes of a squid, and genetically-altered octopuses could be coming soon.

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Cannabis sativa, especially one high in the anti-inflammatory cannabinoid cannabidiol (CBD), has been proposed to modulate gene expression and inflammation and harbour anti-cancer and anti-inflammatory properties. Working under the Health Canada research license, we have developed over 800 new Cannabis sativa lines and extracts and hypothesized that high-CBD C. sativa extracts may be used to modulate ACE2 expression in COVID-19 target tissues. Screening C. sativa extracts using artificial human 3D models of oral, airway, and intestinal tissues, we identified 13 high CBD C. sativa extracts that modulate ACE2 gene expression and ACE2 protein levels. Our initial data suggest that some C. sativa extract down-regulate serine protease TMPRSS2, another critical protein required for SARS-CoV2 entry into host cells.

While our most effective extracts require further large-scale validation, our study is crucial for the future analysis of the effects of medical cannabis on COVID-19. The extracts of our most successful and novel high CBD C. sativa lines, pending further investigation, may become a useful and safe addition to the treatment of COVID-19 as an adjunct therapy. They can be used to develop easy-to-use preventative treatments in the form of mouthwash and throat gargle products for both clinical and at-home use. Such products ought to be tested for their potential to decrease viral entry via the oral mucosa. Given the current dire and rapidly evolving epidemiological situation, every possible therapeutic opportunity and avenue must be considered.

Continue reading “In Search of Preventative Strategies: Novel Anti-Inflammatory High-CBD Cannabis Sativa Extracts Modulate ACE2 Expression in COVID-19 Gateway Tissues” »

This sucks.

Per- and polyfluoroalkyl substances (PFAS), found in many household products and food packages, have raised concerns because of their persistence and possible toxicity to people and wildlife. Because the compounds don’t break down naturally, they have become environmental contaminants. Now, researchers reporting in Environmental Science & Technology have studied the transport of 29 PFAS into and out of the Arctic Ocean, detecting a newer compound for the first time in Arctic seawater.

After studies indicated that two PFAS—PFOA and PFOS—can cause cancer, a compromised immune response and other health problems in lab animals, the two compounds were voluntarily phased out by industry. However, these legacy compounds are still widely detected in the environment. Intended as a safer replacement for PFOA, HFPO-DA (sold under the trade name GenX) is now thought to pose similar health and persistence concerns. Hanna Joerss and colleagues wanted to investigate the long-range, oceanic transport of legacy and replacement PFAS to the Arctic Ocean—a remote body of water connected to the Atlantic Ocean by the Fram Strait, which is located between Svalbard and Greenland.

Continue reading “Newer PFAS compound detected for first time in Arctic seawater” »

An Artificial Intelligence tool to help funders identify specialists to peer-review proposals for emergency research has been developed by Frontiers. It aims to help fast-track the allocation of funding, and in turn, accelerate the scientific response.

An Artificial Intelligence (AI) tool to help funders identify specialists to peer-review proposals for emergency COVID-19 research has been developed. It aims to help fast-track the allocation of funding, and in turn, accelerate the scientific response to the virus.

The open-access publisher Frontiers has specifically developed the recommendation tool to aid funders during the crisis by helping them identify new reviewers. Under normal circumstances, the review process for research funding typically takes place by committee and can take a matter of months. However, since the COVID-19 outbreak, experts have become less available, and the urgency of this situation commands a tighter timeframe.

Continue reading “Artificial intelligence to enable fast-track review of COVID-19 research proposals” »

The most reliable predictor of a relationship’s success is partners’ belief that the other person is fully committed, a Western University-led international research team has found.

Other important factors in a successful relationship include feeling close to, appreciated by, and sexually satisfied with your partner, says the study—the first-ever systematic attempt at using machine-learning algorithms to predict people’s relationship satisfaction.

“Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity,” Western Psychology professor Samantha Joel said. “But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.”

Scientists from the University of Missouri, the University of Illinois and Yale University have demonstrated that a combination of pencils and paper could be used to create on-skin bioelectronic devices that might be used to monitor personal health. They’ve fabricated and evaluated a rich variety of pencil-paper-based bioelectronic devices, ranging from biophysical sensors and sweat biochemical sensors to thermal stimulators, ambient humidity energy harvesters, and transdermal drug-delivery systems.

A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program.

“Humans are good at recognizing anomalies, but they have their own biases or past experience,” said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. “Machines are detached from the whole story. There’s definitely an element of standardizing care.”

To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer.