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Category: neuroscience – Page 325

UCLA Health researchers have published the largest-ever study of families with at least two children with autism, uncovering new risk genes and providing new insights into how genetics influence whether someone develops autism spectrum disorder.

The new study, published July 28 in the Proceedings of the National Academy of Sciences, also provides genetic evidence that language delay and dysfunction should be reconsidered as a core component of autism.

Most genetic studies of autism have focused on families with one child affected by the neurodevelopmental disorder, sometimes excluding families with multiple affected children. As a result, few studies have examined the role of rare inherited variation or its interaction with the combined effect of multiple common genetic variations that contribute to the risk of developing autism.

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The STAR party’s vision for Canada includes the research and development of self sustainable Mobile Airborne Cities; or Airborne Arcologies. Being an obviously semi-long term goal, the objective would be to at first, allocate budgeting towards research and development of components to build this project in a phased manner… and the scaling of the project as technology allows for it.

Phase I: research and development of scalable micro-prototypes.

Phase II: multiple prototype development / testing stages.

Phase III: Final modifications, and testing of Finished Model.

Phase IV: aircity one digital-testing / infrastructure development.

Continue reading “Complex brain cell connections in the cerebellum more common than believed” | >

Researchers from the Tokyo University of Science recently published a study in the journal Artificial Life and Robotics where they explored how machine learning can help detect deception.

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to learn and improve from experience without being explicitly programmed. In other words, it is a method of teaching computers to perform specific tasks by learning from data, patterns, and examples, rather than relying on pre-defined rules.

Detecting deception can be important in various situations, like questioning crime victims or suspects and interviewing patients with mental health issues. Sometimes, human interviewers might struggle to ask the right questions or spot deception accurately.

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The biological roots of autism continue to perplex researchers, despite a growing body of studies looking at an increasing array of genetic, cellular and microbial data. Recently, scientists have homed in on a new and promising area of focus: the microbiome. This collection of microbes that inhabit the human gut has been shown to play a role in autism, but the mechanics of this link have remained awash in ambiguity.

Taking a fresh computational approach to the problem, a study published today, June 26, in Nature Neuroscience sheds new light on the relationship between the microbiome and . This research—which originated at the Simons Foundation’s Autism Research Initiative (SFARI) and involved an innovative reanalysis of dozens of previously published datasets—aligns with a recent, long-term study of autistic individuals that centered on a microbiome-focused treatment intervention. These findings also underscore the importance of longitudinal studies in elucidating the interplay between the microbiome and complex conditions such as autism.

“We were able to harmonize seemingly disparate data from different studies and find a common language with which to unite them. With this, we were able to identify a microbial signature that distinguishes autistic from neurotypical individuals across many studies,” says Jamie Morton, one of the study’s corresponding authors, who began this work while a postdoctoral researcher at the Simons Foundation and is now an independent consultant. “But the bigger point is that going forward, we need robust long-term studies that look at as many datasets as possible and understand how they change when there is a [therapeutic] intervention.”

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When the scaffold is treated with a steroid called fluocinolone acetonide, which protects against inflammation, the resilience of the cells appears to increase, promoting growth of eye cells. These findings are important in the future development of ocular tissue for transplantation into the patient’s eye.

Scientists have found a way to use nanotechnology to create a 3D ‘scaffold’ to grow cells from the retina-paving the way for potential new ways of treating a common cause of blindness.

Researchers, led by Professor Barbara Pierscionek from Anglia Ruskin University (ARU), have been working on a way to successfully grow retinal pigment epithelial (RPE) cells that stay healthy and viable for up to 150 days. RPE cells sit just outside the neural part of the retina and, when damaged, can cause vision to deteriorate.

It is the first time this technology, called ‘electrospinning’, has been used to create a scaffold on which the RPE cells could grow, and could revolutionise treatment for one of age-related macular degeneration, one of the world’s most common vision complaints.

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New research provides evidence that training our heart rate can indirectly influence our emotional memory, making us more likely to remember positive experiences. The study has been published in the journal Applied Psychophysiology and Biofeedback.

The study aimed to explore whether certain brain circuits are responsible for regulating both heart rate and emotion, specifically focusing on the role of the ventromedial prefrontal cortex (vmPFC). Additionally, the researchers were interested in understanding how biofeedback training, which involves providing individuals with real-time physiological feedback and training them to modify their own physiological responses, could impact emotional memory biases.

“There have been many studies showing that people with higher resting HRV tend to experience less negative emotions,” study author Mara Mather told PsyPost. “But most of the research has been correlational and so it is not clear if the individual differences in HRV play any direct role in the emotional differences. Thus, we were interested in whether manipulating HRV could affect people’s emotional biases.”

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