A lung disease called chronic obstructive pulmonary disease (COPD) can have close connections to bacteria in the human gastrointestinal tract, according to new research published in the journal Gut. COPD is a chronic lung disease in which patients have difficulty breathing. It is usually attributed to the inhalation of toxins like long-term cigarette use or exposure to air pollution, for example. Worldwide, COPD is the third leading cause of death. Now we can add it to the long list of conditions that have been associated with the vast community of microbes in the GI tract, called the gut microbiome.
Researchers have shown that specific types of gut bacteria are linked to the development of COPD. While this does not show a cause and effect relationship, the investigators also determined that when fecal bacteria were transferred from healthy mice to mice with COPD, symptoms of COPD were relieved in the recipient mice.
A new study conducted by researchers at the University of Oxford has challenged previously held views that brain preservation in the archaeological record is extremely rare. The team carried out the largest study to date of the global archaeological literature about preserved human brains to compile an archive that exceeds 20-fold the number of brains previously compiled. The findings have been published today in the Proceedings of the Royal Society B.
Researchers from U of T Medicine pinpoint issue that could be hampering common chemotherapy drug ➡️
Researchers at the University of Toronto’s Donnelly Centre for Cellular and Biomolecular Research have found two enzymes that work against the chemotherapy drug gemcitabine, preventing it from effectively treating pancreatic cancer.
The enzymes – APOBEC3C and APOBEC3D – increase during gemcitabine treatment and promote resistance to DNA replication stress in pancreatic cancer cells.
This, in turn, counteracts the effects of gemcitabine and allows for the growth of cancer cells.
The term “artificial general intelligence” (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is “to ensure that artificial general intelligence benefits all of humanity.” DeepMind’s company vision statement notes that “artificial general intelligence…has the potential to drive one of the greatest transformations in history.” AGI is mentioned prominently in the UK government’s National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of “sparks of AGI” in the large language model GPT-4, and current and former Google executives proclaimed that “AGI is already here.” The question of whether GPT-4 is an “AGI algorithm” is at the center of a lawsuit filed by Elon Musk against OpenAI.
Given the pervasiveness of AGI talk in business, government, and the media, one could not be blamed for assuming that the meaning of the term is established and agreed upon. However, the opposite is true: What AGI means, or whether it means anything coherent at all, is hotly debated in the AI community. And the meaning and likely consequences of AGI have become more than just an academic dispute over an arcane term. The world’s biggest tech companies and entire governments are making important decisions on the basis of what they think AGI will entail. But a deep dive into speculations about AGI reveals that many AI practitioners have starkly different views on the nature of intelligence than do those who study human and animal cognition—differences that matter for understanding the present and predicting the likely future of machine intelligence.
The original goal of the AI field was to create machines with general intelligence comparable to that of humans. Early AI pioneers were optimistic: In 1965, Herbert Simon predicted in his book The Shape of Automation for Men and Management that “machines will be capable, within twenty years, of doing any work that a man can do,” and, in a 1970 issue of Life magazine, Marvin Minsky is quoted as declaring that, “In from three to eight years we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight.”
Lonely young adults are more prone to being disengaged from education or employment and perceive themselves as less employable, according to the study published in the journal Social Science and Medicine recently. As a consequence, such individuals tend to get positioned lower on the economic ladder compared to their less lonely counterparts.
Findings revealed that young adults who grappled with loneliness earlier in life encountered challenges in their young adulthood, irrespective of their current loneliness status. This underscores the long-term economic implications of loneliness and the potential economic benefits of addressing loneliness during early adolescence.
When it comes to the cosmic conundrum of how early galaxies grew to become so massive so quickly Gz9p3 could be a real puzzle. Not only is it more massive than expected, but it is around 10 times more massive than other galaxies the JWST has seen in similar eras of the universe’s history.
“Just a couple of years ago, Gz9p3 appeared as a single point of light through the Hubble Space Telescope,” Kit Boyett, team member and a scientist at the University of Melbourne, wrote for the institute’s Pursuit publication. “But by using the JWST we could observe this object as it was 510 million years after the Big Bang, around 13 billion years ago.”
Much-hated Reddit founder and CEO Steve Huffman gifted himself a stunning $193 million compensation package — while unpaid moderators on the platform have yet to see a single dollar, as Variety reports.
It’s an especially pertinent topic given that the company went public at a share price of $34 today, for a valuation of $6.4 billion.
Despite the astonishing successes of quantum mechanics, due to some fundamental problems such as the measurement problem and quantum arrival time problem, the predictions of the theory are in some cases not quite clear and unique.
The measurement and quantum arrival time problems have originated various predictions for the join spatiotemporal distribution of particle detection events, derived from different formulations and interpretations of the quantum theory. By reworking the famous double-slit experiment, the authors propose a realizable setup to probe such predictions.
