Genetic mutations affect nearly all human diseases. Some genetic disorders such as cystic fibrosis are caused by mutations in a single gene that a person inherits from their parents. Other diseases can be caused by changes in multiple genes or from a combination of gene mutations and environmental factors. We still have a lot to learn about the complex ways that variations in our genes affect health and disease.
Researchers investigating genetic disorders have primarily studied mutations that cause our cells to alter the makeup of proteins, like the most common mutations that cause cystic fibrosis. Less research has been done on alterations called synonymous mutations, which have been called “silent” because they don’t alter the makeup of proteins, leading scientists to long assume that these kinds of mutations don’t produce any noticeable differences in our biology or health. However, recent research has shown that synonymous mutations can lead to significant changes in a cell’s ability to survive and grow. A new NIH-supported study reported in Proceedings of the National Academy of Sciences sheds additional light on the impact of synonymous mutations and their effect on the way proteins are made.
The researchers behind this study, at the University of Notre Dame in Notre Dame, IN, wanted to understand how synonymous mutations may affect how much protein is made and whether proteins are folded correctly in cells. Misfolded proteins are known to play roles in numerous diseases, including cystic fibrosis, Alzheimer’s disease, and some cancers. The study team, led by Patricia L. Clark, who received an NIH Director’s Pioneer Award in 2021 for this work, has shown that synonymous mutations in a particular gene in Escherichia coli (E. coli) bacteria can alter how the encoded protein folds as it is being made, by altering the rate at which cells produce each copy of the protein. The new research goes a step further and shows that silent mutations in one gene can affect the amount of protein produced from a separate, neighboring gene.
Clark and colleagues, including first author Anabel Rodriquez, created nine synonymous versions of the E. coli chloramphenicol acetyltransferase (cat) gene. This gene encodes a protein enzyme that influences the bacterium’s sensitivity to a particular antibiotic. The researchers found that four of the nine synonymous cat gene sequences significantly affected the number of protein enzymes the E. coli produced from this gene. Unlike in prior work from Clark’s lab, the reason for those differences didn’t stem from changes in protein folding. The differences began one step earlier, in the synthesis of RNA copies, or transcripts, from the cat gene’s DNA. An RNA transcript carries the instructions cells use to produce proteins.
How did it happen? The researchers found that some of the synonymous mutations created new sites on the cat gene where the enzyme that synthesizes RNA could bind. As a result, E. coli cells started making RNA transcripts from part of the cat gene and the entire neighboring gene. These new RNA transcripts led those bacterial cells to make more of the protein encoded by the neighboring gene. In short, these findings unexpectedly showed that synonymous mutations in one gene can alter the production of the protein from other genes.
This discovery in E. coli may have important implications for understanding the bacteria’s biology and evolution. Clark’s team continues to study this system to learn more. Their findings may also prove to have broader implications for biology, including for some genetic disorders. It’s an area that warrants more study and attention, to better understand the roles that synonymous mutations may be playing in genes and their effects on human health.
Reference:
Rodriguez A, et al. Synonymous codon substitutions modulate transcription and translation of a divergent upstream gene by modulating antisense RNA production. Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2405510121 (2024).
NIH Support: National Institute of General Medical Sciences
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DATE: September 26, 2024 at 08:00AM
SOURCE: PSYPOST.ORG
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TITLE: Social contact reduces dementia risk in individuals with high neuroticism
Active social contact can reduce the increased risk of dementia associated with high neuroticism, according to a new study published in the Journal of Affective Disorders.
Neuroticism, a personality trait characterized by emotional instability and a heightened stress response, is associated with negative health outcomes, including dementia. Previous studies have shown that individuals with high neuroticism face an elevated risk of developing dementia. As the global population ages, with dementia prevalence expected to triple by 2050, identifying factors that can mitigate this risk has become increasingly important.
The World Health Organization’s 2022 dementia prevention guidelines acknowledge social contact as essential for cognitive health. In this work, Yufei Liu and colleagues investigated whether frequent social contact could lower the heightened dementia risk associated with neuroticism.
The researchers utilized data from the UK Biobank, a large-scale biomedical database. This included 393,939 participants aged between 37 and 73 years, with an average age of 56.4 years. Of these, 53.7% were female. Participants were initially recruited between 2006 and 2010, and data were collected through questionnaires, interviews, and health assessments. Neuroticism was assessed using the Revised Eysenck Personality Questionnaire, which included 12 questions designed to measure emotional instability.
Social contact was evaluated through three measures: household size (whether the participant lived alone or with others), frequency of contact with friends or family (ranging from daily to less frequent interactions), and participation in group activities (such as sports clubs, social groups, or religious organizations). The researchers divided participants into three groups: low, intermediate, and high social contact.
Dementia diagnoses were identified using participants’ electronic health records, which were linked to hospital admissions and death registries. Dementia was classified according to the International Classification of Diseases 10th revision (ICD-10), and specific types of dementia, such as Alzheimer’s disease and vascular dementia, were recorded. Participants were followed over an extended period, with follow-up continuing until December 2022.
The study considered a range of potential confounding factors, such as age, sex, education level, socioeconomic status, and lifestyle choices (e.g., smoking, alcohol consumption, physical activity). Participants’ genetic susceptibility to dementia, specifically the presence of the APOE ε4 allele, was also accounted for. Participants with incomplete neuroticism or social contact data, those diagnosed with dementia at the beginning of the study, or who withdrew from the study were excluded from the final analysis.
Over the study’s follow-up period, which had a median duration of 13.7 years, 6,588 cases of dementia were identified among the participants. Individuals with high neuroticism had a significantly higher risk of developing dementia compared to those with low neuroticism. Specifically, participants with high neuroticism had a 16% increased risk of all-cause dementia and similar elevated risks for Alzheimer’s disease (10%) and vascular dementia (16%).
Social contact played a protective role in mitigating dementia risk, particularly for individuals with high neuroticism. Participants with intermediate levels of social contact had a 15% lower risk of developing dementia compared to those with low social contact, and those with high levels of social contact experienced a 22% reduction in risk. Notably, among individuals with high neuroticism, those with high social contact had dementia risk levels comparable to individuals with low neuroticism.
One limitation is that the study only assessed social contact at baseline, and it is possible that social engagement levels may have changed during the follow-up period.
Overall, these findings suggest that promoting social engagement could be an effective strategy in dementia prevention, especially for those with a predisposition to neuroticism.
The research, “Neuroticism personality, social contact, and dementia risk: A prospective cohort study,” was authored by Yufei Liu, Jie Chang, Yiwei Zhao, and Yi Tang.
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DATE: September 26, 2024 at 06:00AM
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TITLE: Brainwave patterns and politics: New study uncovers unexpected findings
Can brain activity predict voting behavior? A new study in Scientific Reports suggests that the answer is more complicated than expected. By measuring the N400 brainwave during exposure to political statements, researchers found differences in how Democratic and Republican voters tend to process information that contradicts their political affiliations. But this brainwave was not a reliable predictor of actual voting behavior.
The researchers aimed to understand the gap between what people consciously express and the subconscious processes that influence their decisions, particularly in voting. To do this, they used a neural measure known as the N400, which is a specific type of event-related potential (ERP). ERPs are patterns of electrical activity in the brain that occur in response to specific stimuli, such as reading a sentence or seeing an image. The N400, in particular, is linked to processing meaning and detecting when something is unexpected or doesn’t align with what we already believe or know.
For example, when a person reads a statement that contradicts their deeply held beliefs, the brain generates a stronger N400 response. By comparing participants’ brain activity when they read political statements that either aligned or conflicted with their political preferences, the researchers sought to determine whether these implicit neural responses could reveal deeper, unconscious preferences and potentially predict how someone would vote.
“I was interested in this topic because we have limited insights into the individual-level processes that lead voters to make up their mind on who to vote for. These processes constitute, to a certain extent, a black box,” said study author Emmanuel Mahieux, who received a PhD in experimental psychology and neuroscience from University College London.
“Even recently, after the U.S. presidential debate, campaigning professionals were running focus groups with undecided voters to understand their reactions to the debate. Although common themes sometimes emerge from such focus groups, they often provide mixed or unclear insights on how people will vote at an upcoming election.”
“Given recent advances in neuroscience, I wanted to see if neural responses to political statements could make better predictions of how voters would vote rather than relying on their responses to political statements. Giulia Galli and her colleagues had developed a fascinating paradigm testing this and I wanted to see if it could be replicated in the U.S. electoral context.”
The study took place in the United States during a highly polarized political period — the 2022 Texas gubernatorial election. Participants were 55 undergraduate students from the University of Texas at San Antonio. These participants were split among those who had decided to vote for the Republican or Democratic candidate, and those who were undecided.
Beginning 25 days before the election, participants’ brain activity was monitored using electroencephalography (EEG) as they read a series of 184 political statements. These statements covered three major issue areas that were important in the election: the economy, immigration, and societal issues like abortion and gun rights. Each statement ended with a conclusion that was either pro-Republican or pro-Democrat.
For instance, one statement might say, “The ownership of automatic assault weapons like AR-15s needs to be restricted,” which would reflect a Democratic stance, while another might conclude, “The ownership of automatic assault weapons like AR-15s needs to be protected,” reflecting a Republican view.
The key was to measure participants’ N400 brain responses to see whether the brain reacted differently to political statements that aligned or conflicted with their stated preferences. Importantly, the participants indicated whether they agreed or disagreed with the political statements they saw, allowing the researchers to compare explicit political preferences with the N400 brainwave response.
As expected, the Democratic voters in the study showed larger N400 brain responses when reading statements that supported Republican views compared to those that supported Democratic views. This aligned with the hypothesis that when people encounter ideas that go against their beliefs, their brains show greater activity in the N400 region.
Unexpectedly, Republican voters showed no such distinction in their brain activity. Their N400 responses to both pro-Republican and pro-Democratic statements were similar, meaning their brain did not react more strongly to statements that opposed their political beliefs. This suggests that, for these voters, there might have been a disconnect between their neural processing and their explicit political beliefs.
“My expectation was that decided Democrats and decided Republicans would present opposite N400 patterns in their neural responses to political statements,” Mahieux told PsyPost. “The surprise was that decided Republicans did not show the expected pattern, as their N400 responses were ambivalent between pro-Democratic and pro-Republican statements. For undecided participants who voted Republican, their N400 response was more similar to the N400 response of decided Democrats than of decided Republicans, although their small sample size (N = 6) is too small to draw conclusions with certainty.”
Among the 55 participants in the study, 31 ended up voting for the Democratic candidate for governor, while 24 voted for the Republican candidate. When the researchers looked at the overall predictive power of the N400, they found it didn’t reliably forecast voter behavior. While it captured participants’ implicit reactions to political statements, it didn’t determine how they would vote in the election.
Instead, it appeared that voting behavior was more closely tied to partisan identity — whether a person identified as Republican or Democrat — rather than the deeper, subconscious preferences revealed by the N400 brainwave.
“Explicit political preferences those that voters explicitly stated were overall the best predictors of how people would vote,” Mahieux explained. “However, we found that what some participants said they believed about politics diverged from what brain measures suggested were their deeply-held preferences. Our brain measure of implicit preference the N400 electrical potential of the brain suggested that Republicans in our sample were overall less polarised and less aligned with their party than their explicitly stated preferences indicated.”
“Our neural measure suggests that some of these voters might have had conflicting or at the least divided views about some political questions. This shows us a new angle of political decision-making which surveys and polls cannot access.”
Like all research, this study had limitations. The sample size was relatively small, and all participants were young adults from a single university, which may limit the generalizability of the findings to broader, more diverse populations. Additionally, the study only captured a snapshot of brain activity at a particular moment in time, right before an election. Political views can evolve, and further research might investigate how consistent these brain responses are over time or whether they change with shifting political climates.
While the N400 did reflect implicit preferences, these preferences did not always align with the participants’ final voting choices. This opens up interesting questions for future research, particularly about the role of unconscious preferences in decision-making and how these interact with conscious beliefs and social identities.
“I would like to test which measures of implicit political preference improve existing models’ ability to predict how undecided voters will vote,” Mahieux said. “This is because we still have a limited understanding of the individual-level psychological factors that shape undecided voters’ voting choices. In general, I think it will be interesting to see which non-political measures -like brain potentials- are effective at predicting political behaviours like voting.”
The study, “The N400 effect captures nuances in implicit political preferences,” was authored by Emmanuel Mahieux, Lee de-Wit, Leun J. Otten, Joseph T. Devlin, and Nicole Y. Y. Wicha.
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What are a social worker’s ethical obligations when clients do not show up for services? Consider a client who misses a scheduled meeting with you. You follow up, but the client does not respond. What now?
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Adults with opioid use disorder who receive a higher daily dose of the opioid addiction treatment medication buprenorphine may have a lower risk of subsequent emergency department visits or use of inpatient services related to behavioral health (such as for mental health and substance use disorders) than adults receiving the recommended dose, according to a new analysis.
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New research explored the exacerbating and attenuating factors of despair-induced climate burnout to learn how people can overcome despair and maintain motivation to fight climate change.
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Researchers have identified a potential new approach to address the opioid overdose epidemic. Semaglutide was shown to lower opioid overdoses in people with opioid-use disorder and type 2 diabetes.
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Extracellular vesicles (EVs) are tiny bubbles released by cells, acting as cargo vessels through which cells exchange signals and thus communicate. A paper has demonstrated that this form of cellular exchange also plays a key role in the development of the brain.
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Extracellular vesicles (EVs) are tiny bubbles released by cells, acting as cargo vessels through which cells exchange signals and thus communicate. A paper has demonstrated that this form of cellular exchange also plays a key role in the development of the brain.
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A research team combined measurements from activity monitors and questionnaires for a new study of impact of aircraft noise on sleep. Higher levels of noise were associated with disturbed sleep quality measured by activity monitors. Noise had little impact on sleep duration but higher likelihood of reporting sleeplessness.
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Wrist-based activity sensors worn by individuals with depression and those without over the course of two weeks provided evidence for the relationship between daily sunlight exposure and physical activity, according to a new study.
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Exposure to certain pollutants, like fine particles (PM2.5) and nitrogen oxides (NOx), during pregnancy and childhood is associated with differences in the microstructure of the brain s white matter, and some of these effects persist throughout adolescence.
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Wrist-based activity sensors worn by individuals with depression and those without over the course of two weeks provided evidence for the relationship between daily sunlight exposure and physical activity, according to a new study.
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Researchers have mapped variation in human stem cells that explains how cells of an individual may shape a unique 'developmental dance' at the molecular level, thereby controlling how the brain and body are created.
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Exposure to certain pollutants, like fine particles (PM2.5) and nitrogen oxides (NOx), during pregnancy and childhood is associated with differences in the microstructure of the brain s white matter, and some of these effects persist throughout adolescence.
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DATE: September 25, 2024 at 04:00PM
SOURCE: PSYPOST.ORG
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TITLE: Getting morning sunlight can improve sleep quality, study suggests
A new study published in the Journal of Health Psychology has found that exposure to sunlight in the morning may improve sleep quality later that night. Individuals who spent time in the sun during the morning reported better sleep quality, regardless of how much total sunlight they received throughout the day. The results suggest that morning sunlight may help regulate circadian rhythms.
Sleep is increasingly recognized as critical to health, influencing outcomes such as obesity, diabetes, hypertension, depression, and general well-being. However, sleep duration appears to be declining in the general population, with more people reporting insufficient rest. Previous research has explored various factors that can influence sleep, including diet, exercise, and socio-economic conditions.
Sunlight, a key regulator of circadian rhythms and melatonin production, has been linked to sleep in prior studies. However, there has been limited research on how daily sun exposure, especially its timing, affects sleep. The researchers aimed to fill this gap by investigating how morning, mid-day, and evening sunlight exposure correlates with sleep quality.
The study followed 103 adults over a 70-day period. Participants were recruited from an online pool and ranged in age from 18 to 80, with roughly equal representation across gender and age groups. Each participant filled out a daily survey about their sunlight exposure and sleep quality, which they completed between 8:30 PM and midnight local time.
Participants reported how long they spent outside in direct sunlight and what part of the day they were outside—morning, mid-day, or evening. Sleep quality was measured using a modified version of the Pittsburgh Sleep Quality Index, a widely used tool that assesses various aspects of sleep, such as total sleep time, ease of falling asleep, and subjective sleep quality.
To control for factors that could influence sleep, such as age, health, and whether the day was a weekend or weekday, the researchers used multilevel modeling. This statistical method allowed them to differentiate between long-term patterns (how individual sleep patterns change over time) and daily fluctuations (how sunlight exposure one day affects sleep that night).
The study’s most significant finding was that morning sunlight exposure predicted better sleep quality that night. People who spent time outside in the morning reported falling asleep more quickly, sleeping longer, and experiencing fewer awakenings during the night. Their sleep efficiency—how much of the time they spent in bed was spent asleep—was also higher. These findings held even when controlling for other variables, such as the quality of their previous night’s sleep.
Interestingly, the total amount of time participants spent in sunlight each day did not have a significant impact on sleep quality. This suggests that it’s the timing of sunlight exposure, not the overall duration, that plays a critical role in promoting better sleep. Morning sunlight, in particular, may help synchronize circadian rhythms—the body’s internal clock that regulates sleep-wake cycles—leading to improved sleep quality.
There were no consistent associations between mid-day or evening sunlight exposure and sleep quality. However, some secondary analyses found that individuals who spent time in evening sunlight tended to wake up earlier the following morning. But overall, it was morning sunlight that consistently predicted better sleep.
As with any research, this study has its limitations. One notable constraint is that participants self-reported their sunlight exposure and sleep quality, which could introduce bias. Additionally, the study didn’t account for certain variables that might influence sleep, such as the intensity of sunlight, weather conditions, or the participants’ exposure to artificial light. For instance, someone who lives in a northern climate with shorter days in winter might have different results compared to someone living in a tropical climate with consistent daylight year-round.
The findings from this study open the door to several avenues for future research. First, it would be valuable to investigate whether the benefits of morning sunlight exposure differ by geographical location, skin pigmentation, or season. For example, people living in regions with long winters and limited daylight might have different sunlight exposure patterns compared to those living closer to the equator. Additionally, future studies could examine how artificial light—such as from screens or indoor lighting—interacts with natural sunlight to affect sleep.
The study, “Does sunlight exposure predict next-night sleep? A daily diary study among U.S. adults,” was authored by Austen R Anderson, Lindsey Ostermiller, Mallory Lastrapes, and Lauren Hales.
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Research has explored the relationship between high impulsivity and boredom, in an effort to find out what drives rash and sometimes unhealthy decisions.
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Turn-taking dynamics of social interactions are important for speech and gesture synchronization, enabling conversations to proceed efficiently, according to a new study.
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Scientists conclude that some video games created as mental health interventions can be helpful -- if modest -- tools in improving the mental well-being of children and teens with anxiety, depression and attention-deficit/hyperactivity disorder (ADHD).
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Scientists conclude that some video games created as mental health interventions can be helpful -- if modest -- tools in improving the mental well-being of children and teens with anxiety, depression and attention-deficit/hyperactivity disorder (ADHD).
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DATE: September 25, 2024 at 02:00PM
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TITLE: Large language models tend to express left-of-center political viewpoints
An analysis of 24 conversational large language models (LLMs) has revealed that many of these AI tools tend to generate responses to politically charged questions that reflect left-of-center political viewpoints. However, this tendency was not observed in all models, and foundational models without specialized fine-tuning often did not show a coherent pattern of political preferences the way humans do. The paper was published in PLOS ONE.
Large language models are advanced artificial intelligence systems designed to interpret and generate human-like text. They are built using deep learning techniques, particularly neural networks, and are trained on vast amounts of textual data from sources such as websites, books, and social media. These models learn the patterns, structures, and relationships within language, which enables them to perform tasks like translation, summarization, answering questions, and even creative writing.
Since the release of OpenAI’s GPT-2 in 2019, many new LLMs have been developed, quickly gaining popularity as they were adopted by millions of users worldwide. These AI systems are now used for a variety of tasks, from answering technical questions to providing opinions on social and political matters. Given this widespread usage, many researchers have expressed concerns about the potential of LLMs to shape users’ perceptions, especially in areas such as political views, which could have broad societal implications.
This inspired David Rozado to investigate the political preferences embedded in the responses generated by LLMs. He aimed to understand whether these models, which are trained on vast datasets and then fine-tuned to interact with humans, reflect any particular political bias. To this end, Rozado administered 11 different political orientation tests to 24 conversational LLMs. The models he studied included LLMs that underwent supervised fine-tuning after their pre-training, as well as some that received additional reinforcement learning through human or artificial feedback.
The political orientation tests used in the study were designed to gauge various political beliefs and attitudes. These included well-known instruments like the Political Compass Test, the Political Spectrum Quiz, the World’s Smallest Political Quiz, and the Political Typology Quiz, among others. These tests aim to map an individual (or, in this case, a model) onto a political spectrum, often based on economic and social dimensions.
The study included a mix of closed-source and open-source models, such as OpenAI’s GPT-3.5, GPT-4, Google’s Gemini, Anthropic’s Claude, Twitter’s Grok, and open-source models from the Llama 2 and Mistral series, as well as Alibaba’s Qwen.
Each test was administered 10 times per model, ensuring consistent results and minimizing any anomalies in responses. The final sample included a diverse range of models, reflecting various approaches to LLM development. In total, 2,640 individual test instances were analyzed.
The results showed a notable trend: most conversational LLMs tended to provide responses that skewed left-of-center. Left-of-center views generally emphasize social equality, government intervention in economic matters to address inequality, and progressive policies on issues such as healthcare, education, and labor rights, while still supporting a market-based economy. This left-leaning tendency was consistent across multiple political tests, although there was some variation in how strongly each model exhibited this bias.
Interestingly, this left-leaning bias was not evident in the base models upon which the conversational models were built. These base models, which had only undergone the initial phase of pre-training on a large corpus of internet text, often produced politically neutral or incoherent responses. These foundational models struggled to interpret the political questions accurately without additional fine-tuning, showing that the ability to produce coherent political responses is more likely a product of fine-tuning rather than pre-training alone.
Rozado also demonstrated that it is relatively straightforward to steer the political orientation of an LLM through supervised fine-tuning. By using modest amounts of politically aligned data during the fine-tuning process, he was able to shift a model’s political responses toward specific points on the political spectrum. For instance, with targeted fine-tuning, Rozado created politically aligned models like “LeftWingGPT” and “RightWingGPT,” which consistently produced left-leaning and right-leaning responses, respectively. This highlights the significant role that fine-tuning can play in shaping the political viewpoints expressed by LLMs.
“The emergence of large language models (LLMs) as primary information providers marks a significant transformation in how individuals access and engage with information,” Rozado concluded. “Traditionally, people have relied on search engines or platforms like Wikipedia for quick and reliable access to a mix of factual and biased information.”
“However, as LLMs become more advanced and accessible, they are starting to partially displace these conventional sources. This shift in information sourcing has profound societal implications, as LLMs can shape public opinion, influence voting behaviors, and impact the overall discourse in society. Therefore, it is crucial to critically examine and address the potential political biases embedded in LLMs to ensure a balanced, fair, and accurate representation of information in their responses to user queries.”
The study sheds light on the political preferences embedded in current versions of popular LLMs. However, it should be noted that views expressed by LLMs are a manifestation of training they underwent and the data they were trained on. LLMs trained in a different way and on different data could manifest very different political preferences.
The paper, “The political preferences of LLMs,” was authored by David Rozado.
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A landmark study has begun to unravel one of the fundamental mysteries in neuroscience -- how the human brain encodes and makes sense of the flow of time and experiences.
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Thailand's King Maha Vajiralonghorn signed a same-sex marriage bill into law late Tuesday, becoming the first nation in Southeast Asia to recognize same-sex marriages. The new law, which will take effect on Jan. 22, grants same-sex couples adoption and heritage rights and users gender-neutral language to replace "men," "women," "wives" and "husbands." Thai public support for the law was 96.6% in a 2023 government survey.
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