Lifeboat Foundation

Findings could help explain how air pollutants interact with the atmosphere.

Researchers at the University of British Columbia, University of California Irvine, and McGill University have discovered three liquid phases in aerosol particles, changing our understanding of air pollutants in the Earth’s atmosphere.

While aerosol particles were known to contain up to two liquid phases, the discovery of an additional liquid phase may be important to providing more accurate atmospheric models and climate predictions. The study was published recently in PNAS.

But being such effective survivors may come at a cost.

A group of Cancer Research UK-funded scientists are beginning to discover new vulnerabilities in cancer cells, which emerge when they enter “survival mode.”

Gotta catch them all because this one may cause legionnaires diesease.

“Institute of Zoology have named one of the newly discovered bacteria ‘Pokemonas’ because they live in spherical amoebae, comparable to Pokémon in the video game, which are caught in balls.”

A research team at the University of Cologne has discovered previously undescribed bacteria in amoebae that are related to Legionella and may even cause disease. The researchers from Professor Dr. Michael Bonkowski’s working group at the Institute of Zoology have named one of the newly discovered bacteria ‘Pokemonas’ because they live in spherical amoebae, comparable to Pokémon in the video game, which are caught in balls. The results of their research have been published in the journal Frontiers in Cellular and Infection Microbiology.

Continue reading “‘Pokemonas’: Bacteria related to lung parasites discovered, named after Pokémon” »

When you think of night-vision goggles, you probably imagine the pitch black of night being illuminated in a sea of green that helps improve visibility. That’s ancient technology now as the US Army’s Lancer Brigade of Joint Base Lewis–McChord demonstrates what soldiers see through the military’s latest and greatest night vision goggles. The Predator would be jealous.

A new study found that perfectionist thinking patterns contributed to posttraumatic stress disorder (PTSD) and generalized anxiety disorder (GAD) symptoms, over and above several known control variables. The findings were published in Cognitive Behaviour Therapy.

Perfectionism involves a desire to perform to the highest standards without allowing room for failure. People with perfectionist beliefs tend to be overly self-critical and put pressure on themselves to perform flawlessly at all times. While perfectionism is often seen as a favorable trait, the attribute has been linked to numerous anxiety disorders such as obsessive compulsive disorder (OCD) and social anxiety disorder (SAD).

Researchers have recently begun exploring the thought patterns that characterize perfectionism — called perfectionist cognitions (PC). As study author Jeremy Tyler and his team say, perfectionist cognitions include expectations about achieving perfection such as, “I can’t stand to make mistakes.” These cognitions have been linked to dysfunctional mental health symptoms like obsessions, distress, and anxiety. However, these associations have yet to be explored among a clinical population.

Targeting a pathway that is essential for the survival of certain types of acute myeloid leukaemia could provide a new therapy avenue for patients, the latest research has found.

Researchers from the Wellcome Sanger Institute found that a specific genetic mutation, which is linked with poor prognosis in blood cancer, is involved in the development of the disease when combined with other mutations in mice and human cell lines.

The study, published today (30th April) in Nature Communications, provides a greater understanding of how the loss-of-function mutation in the CUX1 gene leads to the development and survival of acute myeloid leukaemia. The findings suggest that targeting a pathway that is essential for these cancer cells to continue growing could lead to new targeted therapies for some patients.

California is set to be home to two new compressed-air energy storage facilities – each claiming the crown for the world’s largest non-hydro energy storage system. Developed by Hydrostor, the facilities will have an output of 500 MW and be capable of storing 4 GWh of energy.

As the world shifts towards renewable energy, grid-scale storage is becoming ever more crucial. Getting carbon emissions to net-zero will require a patchwork of technologies to smooth out unpredictable and inconvenient generation curves, with pumped hydro, huge lithium-ion batteries, tanks full of molten salt or silicon, thermal bricks, or heavy blocks stacked up in towers or suspended in mineshafts all in the mix.

Continue reading “World’s largest compressed air grid ‘batteries’ will store up to 10GWh” »

One of the interesting consequences of the emergent upshift in visual systems is that all streetlights, car headlights and other external sources of lighting will no longer be needed within around a decade. This will not only make astronomers happy, since they will be able to see the dark skies again but will simplify urban infrastructure. The three convergent elements making this change of affairs come about are the following:

1) Quanta Image Sensors, whether of the SPAD or the CIS-QIS versions are expected to become widely available within 5 to 10 years. Unlike the CMOS image sensors in billions of cell-phone cameras, which only register packets of the incoming light, these sensors can register single photons of light. The most versatile of these are the QIS sensors being developed by Fossum—who also developed the CMOS sensor—wherein a single jot\.

Continue reading “Advances in Detectors: The Quanta image sensor (QIS): Making every photon count” »

Machine learning is capable of doing all sorts of things as long as you have the data to teach it how. That’s not always easy, and researchers are always looking for a way to add a bit of “common sense” to AI so you don’t have to show it 500 pictures of a cat before it gets it. Facebook’s newest research takes a big step toward reducing the data bottleneck.

The company’s formidable AI research division has been working for years now on how to advance and scale things like advanced computer vision algorithms, and has made steady progress, generally shared with the rest of the research community. One interesting development Facebook has pursued in particular is what’s called “semi-supervised learning.”

Generally when you think of training an AI, you think of something like the aforementioned 500 pictures of cats — images that have been selected and labeled (which can mean outlining the cat, putting a box around the cat or just saying there’s a cat in there somewhere) so that the machine learning system can put together an algorithm to automate the process of cat recognition. Naturally if you want to do dogs or horses, you need 500 dog pictures, 500 horse pictures, etc. — it scales linearly, which is a word you never want to see in tech.