Latest news with #DepartmentofBiologicalSciences
Jordan News
3 days ago
- Health
- Jordan News
Official Study: Drinking Coffee at Night Increases Impulsive Behavior—Especially in Females - Jordan News
A recent scientific study conducted by researchers at the University of Texas has revealed that consuming caffeine, particularly at night, may lead to an increase in impulsive behavior, potentially resulting in reckless actions, especially among females. اضافة اعلان The findings, published in the journal iScience, used fruit flies (Drosophila) as a model to explore how nighttime caffeine intake affects behavioral control. The species was chosen due to its genetic and neurological similarities to humans, making it a reliable model for studying complex behaviors. Study Details: According to Dr. Paul Sabandal, assistant professor in the Department of Biological Sciences at the University of Texas, caffeine is the most widely consumed psychoactive substance in the world, with about 85% of U.S. adults using it regularly. The study was designed to explore whether the timing of caffeine intake plays a role in behavioral outcomes. Researchers introduced caffeine into the fruit flies' diet under various conditions, including: Different doses of caffeine Daytime vs. nighttime consumption Sleep deprivation The team then assessed impulsivity by measuring the flies' ability to restrain movement in response to strong air puffs—a method used to simulate a stressful stimulus. Key Findings: Flies that consumed caffeine at night were significantly more impulsive, failing to restrain movement and instead displaying reckless flying behaviors. In contrast, flies that consumed caffeine during the daytime did not exhibit the same level of impulsivity. Gender Differences: Interestingly, the effects were more pronounced in female flies, despite both sexes having similar caffeine levels in their bodies. Researcher Kyung-An Han noted that although female flies do not possess human hormones such as estrogen, this suggests that genetic or physiological factors may make females more sensitive to caffeine's behavioral effects. Han emphasized that understanding these mechanisms could offer deeper insights into how circadian biology and sex-based physiological differences influence caffeine's impact on behavior. Caution and Implications: The research team warned that the findings may have real-world implications for: Night shift workers Healthcare professionals Military personnel —especially females in these groups, who may be more vulnerable to impulsive or risky behavior due to nighttime caffeine consumption. The study concludes with a recommendation to reconsider the timing of caffeine intake, as it may have a significant effect on behavioral control, particularly during night hours.
Hans India
14-07-2025
- Health
- Hans India
AI decodes gut bacteria to provide clues about health
New Delhi: For the first time, researchers from the University of Tokyo have used a special kind of artificial intelligence (AI) called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify. Gut bacteria are known to be a key factor in many health-related concerns. The human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. 'The problem is that we're only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,' said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences in a paper published in Briefings in Bioinformatics. By accurately mapping these bacteria-chemical relationships, we could potentially develop personalised treatments, Dang mentioned. 'Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.' The system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers. 'When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns,' Dang explained. As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimised to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it. 'We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet,' said Dang.
Time of India
07-07-2025
- Health
- Time of India
Here is how AI can help to understand gut bacteria
Gut bacteria are considered to be a key factor in many health-related issues. However, the number and variety of them are vast, as are the ways in which they interact with the body's chemistry and each other. For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify. The human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. Technically, you're carrying around more cells that aren't you than are. Food for thought. And speaking of food, these gut bacteria are, of course, responsible for some aspects of digestion, though what's surprising to some is how they can relate to many other aspects of human health as well. The bacteria are incredibly varied and also produce and modify a bewildering number of different chemicals called metabolites. These act like molecular messengers, permeating your body, affecting everything from your immune system and metabolism to your brain function and mood. Needless to say, there's much to gain by understanding gut bacteria. "The problem is that we're only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases," said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences, adding, "By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases." There are uncountably many and varied bacteria and metabolites, and therefore far more relationships between these things. Gathering data on this alone is a monumental undertaking, but unpicking that data to find interesting patterns that might betray some useful function is even more so. To do this, Dang and his team decided to explore the use of state-of-the art artificial intelligence (AI) tools. "Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers," said Dang. "When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns." As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimized to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it. Other limitations at present include that the system benefits from having more data about the gut bacteria than the metabolites they produce; when there's insufficient bacteria data, the accuracy drops. Also, VBayesMM assumes the microbes act independently, but in reality, gut bacteria interact in an incredibly complex number of ways. "We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet," said Dang. "We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial 'family tree' relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications."
Economic Times
06-07-2025
- Health
- Economic Times
Here is how AI can help to understand gut bacteria
TIL Creatives Gut bacteria are considered to be a key factor in many health-related issues. However, the number and variety of them are vast, as are the ways in which they interact with the body's chemistry and each other. For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify. The human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. Technically, you're carrying around more cells that aren't you than are. Food for thought. And speaking of food, these gut bacteria are, of course, responsible for some aspects of digestion, though what's surprising to some is how they can relate to many other aspects of human health as well. The bacteria are incredibly varied and also produce and modify a bewildering number of different chemicals called metabolites. These act like molecular messengers, permeating your body, affecting everything from your immune system and metabolism to your brain function and mood. Needless to say, there's much to gain by understanding gut bacteria."The problem is that we're only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases," said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences, adding, "By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases."There are uncountably many and varied bacteria and metabolites, and therefore far more relationships between these things. Gathering data on this alone is a monumental undertaking, but unpicking that data to find interesting patterns that might betray some useful function is even more so. To do this, Dang and his team decided to explore the use of state-of-the art artificial intelligence (AI) tools."Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers," said Dang. "When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns."As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimized to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it. Other limitations at present include that the system benefits from having more data about the gut bacteria than the metabolites they produce; when there's insufficient bacteria data, the accuracy drops. Also, VBayesMM assumes the microbes act independently, but in reality, gut bacteria interact in an incredibly complex number of ways."We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet," said Dang. "We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial 'family tree' relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications." Elevate your knowledge and leadership skills at a cost cheaper than your daily tea. 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Time of India
06-07-2025
- Health
- Time of India
Here is how AI can help to understand gut bacteria
Academy Empower your mind, elevate your skills Gut bacteria are considered to be a key factor in many health-related issues. However, the number and variety of them are vast, as are the ways in which they interact with the body's chemistry and each the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. Technically, you're carrying around more cells that aren't you than are. Food for thought. And speaking of food, these gut bacteria are, of course, responsible for some aspects of digestion, though what's surprising to some is how they can relate to many other aspects of human health as bacteria are incredibly varied and also produce and modify a bewildering number of different chemicals called metabolites. These act like molecular messengers, permeating your body, affecting everything from your immune system and metabolism to your brain function and mood. Needless to say, there's much to gain by understanding gut bacteria."The problem is that we're only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases," said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences, adding, "By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases."There are uncountably many and varied bacteria and metabolites, and therefore far more relationships between these things. Gathering data on this alone is a monumental undertaking, but unpicking that data to find interesting patterns that might betray some useful function is even more so. To do this, Dang and his team decided to explore the use of state-of-the art artificial intelligence (AI) tools."Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers," said Dang. "When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns."As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimized to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it. Other limitations at present include that the system benefits from having more data about the gut bacteria than the metabolites they produce; when there's insufficient bacteria data, the accuracy drops. Also, VBayesMM assumes the microbes act independently, but in reality, gut bacteria interact in an incredibly complex number of ways."We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet," said Dang. "We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial 'family tree' relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications."
