Investigators identified 15 factors that affect risk for young-onset dementia.
Limited data are available on risk factors for young-onset dementia. In this study, researchers assessed 39 potential risk factors for young-onset dementia from data in the UK Biobank. Participants 65 years of age or older without a dementia diagnosis were included in the analysis. Potential risk factors were grouped into sociodemographic factors, genetic factors, lifestyle factors, environmental factors, blood marker factors, cardiometabolic factors, psychiatric factors, and other risk factors.
Among 359,052 participants, the mean age at baseline was 55 years and 55% were women. There were 485 incident all-cause young-onset dementia cases after a mean follow-up of 8 years. Incident young-onset dementia increased with age and was more common in men. Fewer years of formal education, lower socioeconomic status, the presence of two apolipoprotein E ℇ4 alleles, no alcohol use, alcohol use disorder, social isolation, vitamin D deficiency (1 mg/dL), lower handgrip strength, hearing impairment, orthostatic hypotension, stroke, diabetes, heart disease, and depression were associated with higher risk for young-onset dementia in fully adjusted models. Men with diabetes were more likely to have young-onset dementia than men without diabetes, and women with high C-reactive protein were more likely to have young-onset dementia than women with low C-reactive protein levels.
The largest-ever humanitarian intervention in early childhood education shows that remote learning can produce results comparable to a year of in-person teaching.
Autoimmune disease occurs from the body’s immune system attacking its healthy cells. Unfortunately, the mechanism that would normally prevent autoimmunity is not present in some individuals. T cells are the immune cell population responsible for killing or lysing invading pathogens. In the context of autoimmunity, T cells attack and lyse healthy cells. The thymus gland educates or prepares T cells to become activated and target foreign pathogens. T cells are exposed to different molecules and surface markers which further train these cells on how to respond when they come into contact with foreign markers. Autoimmune disorders are rare and can often be detected in children. However, there are limited treatment options, and a cure has not been found. Researchers are currently working to better treat autoimmune disorders and improve the quality of life in patients.
A recent article published in Nature, by a team led by Dr. Thomas Korn, reported a previously unknown mechanism underlying autoimmune disease. Korn is a Professor of Experimental Nueroimmunology at the Technical University of Munich (TUM) and Principal Investigator at the Maximilian University of Munich (LMU). His lab focuses on T cell biology and the underlying mechanisms of autoimmune disorders. Korn and others demonstrated that another immune cell population, B cells, aid in T cell education in the thymus gland. Korn and others point out that B cells are part of T cell development and play a critical role in autoimmune disorder.
Researchers used both animal models and human tissue samples to conduct their research to investigate T cell development. The autoimmune disorder Korn and his team used as a model is known as neuromyelitis optica, which is similar to multiple sclerosis (MS). Researchers chose this specific model due to the well-known fact that T cells respond to the protein AQP4 in this autoimmune disorder. Interestingly, AQP4 is highly expressed in the nervous system, which becomes the target of autoimmunity. Researchers discovered that B cells also express AQP4, which present this protein to the T cells in the thymus. Interestingly, if the B cells did not express AQP4, then T cells would not become reactive to the surface protein and target healthy nervous system cells. Epithelial cells also expressed the AQP4 protein and resulted in the same autoimmune reaction. However, B cells were found to significantly impact T cell development compared to other cells in the thymus.
For ‘deeper reading’ among children aged 10–12, paper trumps screens. What does it mean when schools are going digital?
Rocky89 / E+ via getty images.
The Department of Education’s most recent study, declared in June, was surely sensational: it found that text comprehension skills of 13-year-olds had denied an average of four points since the Covid-affected schools in the academic year 2019–2020, and more alarmingly that the average drop was seven points compared with the 2012 figure. The results for the worst-performing students fell below the reading skill level recorded in 1971, when the first national study was conducted.
The electron is the basic unit of electricity, as it carries a single negative charge. This is what we’re taught in high school physics, and it is overwhelmingly the case in most materials in nature.
But in very special states of matter, electrons can splinter into fractions of their whole. This phenomenon, known as “fractional charge,” is exceedingly rare, and if it can be corralled and controlled, the exotic electronic state could help to build resilient, fault-tolerant quantum computers.
To date, this effect, known to physicists as the “fractional quantum Hall effect,” has been observed a handful of times, and mostly under very high, carefully maintained magnetic fields. Only recently have scientists seen the effect in a material that did not require such powerful magnetic manipulation.
The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts & Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.
0:00:00 — 0:07:20 Opening remarks and introduction. 0:07:21 — 0:08:43 Overview. 0:08:44 — 0:20:08 Two different ways to do computation. 0:20:09 — 0:30:11 Do large language models really understand what they are saying? 0:30:12 — 0:49:50 The first neural net language model and how it works. 0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence? 0:57:25 — 1:03:18 Does digital intelligence have subjective experience? 1:03:19 — 1:55:36 Q&A 1:55:37 — 1:58:37 Closing remarks.
Talk title: “Will digital intelligence replace biological intelligence?”
Abstract: Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but in return they allow exactly the same model to be run on physically different pieces of hardware, which makes the model immortal. For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and mimic biology by using very low power analog computation that makes use of the idiosynchratic properties of a particular piece of hardware. This requires a learning algorithm that can make use of the analog properties without having a good model of those properties. Using the idiosynchratic analog properties of the hardware makes the computation mortal. When the hardware dies, so does the learned knowledge. The knowledge can be transferred to a younger analog computer by getting the younger computer to mimic the outputs of the older one but education is a slow and painful process. By contrast, digital computation makes it possible to run many copies of exactly the same model on different pieces of hardware. Thousands of identical digital agents can look at thousands of different datasets and share what they have learned very efficiently by averaging their weight changes. That is why chatbots like GPT-4 and Gemini can learn thousands of times more than any one person. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads me to believe that large-scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us. The fact that digital intelligences are immortal and did not evolve should make them less susceptible to religion and wars, but if a digital super-intelligence ever wanted to take control it is unlikely that we could stop it, so the most urgent research question in AI is how to ensure that they never want to take control.
About Geoffrey Hinton.
Geoffrey Hinton received his PhD in artificial intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto, where he is now an emeritus professor. In 2013, Google acquired Hinton’s neural networks startup, DNN research, which developed out of his research at U of T. Subsequently, Hinton was a Vice President and Engineering Fellow at Google until 2023. He is a founder of the Vector Institute for Artificial Intelligence where he continues to serve as Chief Scientific Adviser.
This timelapse of future technology begins with 2 Starships, launched to resupply the International Space Station. But how far into the future do you want to go?
Tesla Bots will be sent to work on the Moon, and A.I. chat bots will guide people into dreams that they can control (lucid dreams). And what happens when humanity forms a deeper understanding of dark energy, worm holes, and black holes. What type of new technologies could this advanced knowledge develop? Could SpaceX launch 100 Artificial Intelligence Starships, spread across our Solar System and beyond into Interstellar space, working together to form a cosmic internet, creating the Encyclopedia of the Galaxy. Could Einstein’s equations lead to technologies in teleportation, and laboratory grown black holes.
Other topics covered in this sci-fi documentary video include: the building of super projects made possible by advancing fusion energy, the possibilities of brain chips, new age space technology and spacecraft such as a hover bike developed for the Moon in 2050, Mars colonization, and technology predictions based on black holes, biotechnology, and when will humanity become a Kardashev Type 1, and then Type 2 Civilization.
To see more of Venture City and to access the ‘The Future Archive Files’…
• Timelapse of Future Technology (Master List) • Encyclopedia of the Future (Entries)
