It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Friday, May 02, 2025
Tunnel vision during planning can lead us to neglect negative consequences, but this cognitive bias can be addressed by simply prompting people to explicitly consider them
Tunnel vision during planning can lead us to neglect negative consequences, but this cognitive bias can be addressed by simply prompting people to explicitly consider them.
Tunnel vision during planning can lead us to neglect negative consequences, but this cognitive bias can be addressed by simply prompting people to explicitly consider them
Side effects may include: Consequence neglect in generating solutions
Article Publication Date
30-Apr-2025
Critically endangered axolotls bred in captivity appear able to survive release into both artificial and restored Mexican wetlands, but may need specific temperatures to thrive
Credit: Dr. David Schneider, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
Critically endangered axolotls bred in captivity appear able to survive release into both artificial and restored Mexican wetlands, but may need specific temperatures to thrive
Article title: Movement ecology of captive-bred axolotls in restored and artificial wetlands: Conservation insights for amphibian reintroductions and translocations
Author countries: Mexico
Funding: This project was funded by UNAM PAPIIT No. 705 IV200117 and IV210117 Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica (PAPIIT-IV200117) AGR received a postdoctoral research grant from PAPIIT IV200117 and IV210117. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Credit: Rafael AS Martins, Unsplash, CC0 (https://creativecommons.org/publicdomain/zero/1.0/)
Human populations need at least 2.7 children per woman – a much higher fertility rate than previously believed – to reliably avoid long-term extinction, according to a new study published April 30, 2025 in the open-access journal PLOS One by Takuya Okabe of Shizuoka University, Japan, and colleagues.
While a fertility rate of 2.1 children per woman is often considered the replacement level needed to sustain a population, this figure doesn’t account for random differences in how many children people have – as well as mortality rates, sex ratios, and the probability that some adults never have children. In small populations, these chance variations can wipe out entire family lineages. In the new study, researchers used mathematical models to examine how this demographic variability affects the survival of populations over many generations.
The study found that, due to random fluctuations in birth numbers, a fertility rate of at least 2.7 children per woman is needed to reliably avoid eventual extinction – especially in small populations. However, a female-biased birth ratio, with more females than males born, reduces the extinction risk, helping more lineages survive over time. This insight may help explain a long-observed evolutionary phenomenon: under severe conditions – such as war, famine, or environmental disruption – more females tend to be born than males. It also suggests that, while extinction isn’t imminent in large developed populations, most family lineages will eventually fade out.
The authors conclude that true population sustainability – as well as the sustainability of languages, cultural traditions, and diverse family lineages – requires rethinking conventional fertility targets. The findings also have implications for conservation efforts of endangered species in which target fertility rates are set, they point out.
Diane Carmeliza N. Cuaresma adds, "Considering stochasticity in fertility and mortality rates, and sex ratios, a fertility rate higher than the standard replacement level is necessary to ensure sustainability of our population."
In your coverage, please use this URL to provide access to the freely available article in PLOS One: https://plos.io/4lu0M6h
Citation: Cuaresma DCN, Ito H, Arima H, Yoshimura J, Morita S, Okabe T (2025) Threshold fertility for the avoidance of extinction under critical conditions. PLoS ONE 20(4): e0322174. https://doi.org/10.1371/journal.pone.0322174
Author countries: Japan, Philippines
Funding: Japan Society for the Promotion of Science (JSPS) KAKENHI grant nos. 23KK0210 and 21H01575 (HI), 21K21115 (HA), 21K03387 (SM), and 21K12047 (TO) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Threshold fertility for the avoidance of extinction under critical conditions
Article Publication Date
30-Apr-2025
Setting, acute reaction and mental health history shape ayahuasca's longer-term psychological effects
Some acute post-ayahuasca “adverse effects” like visual distortions were associated with better reported mental health at a later date, while other adverse effects like feeling isolated or energetically attacked were associated with worse mental health
Mounting evidence supports ayahuasca’s potential to improve mental health, but its long-term effects are shaped by both individual mental health history and the context in which the psychedelic is used, according to a study published on April 30, 2025 in the open-access journal PLOS Mental Health by Óscar Andión from Research Sherpas, Spain; José Carlos Bouso from the International Centre for Ethnobotanical Education, Research, and Services (ICEERS) and the University of Rovira i Virgili, Spain; Daniel Perkins from the University of Melbourne and Swinburne University; and colleagues.
Ayahuasca, a psychedelic medicine traditionally used by Indigenous communities in South America, has received increasing interest from Western researchers and clinicians for its potential mental health benefits, but its potential risks and adverse effects remain understudied. In a previous review of adverse effects reported in a global survey of ayahuasca ceremony participants, José Carlos Bouso, Andión, and colleagues found that over half reported adverse mental states after ayahuasca use, with greater adverse experiences associated with a history of mental illness and using the drug in non-traditional settings. Importantly, potential adverse effects reported ranged from visual distortions or hallucinations to “feeling down, depressed, or hopeless”, “feeling disconnected or alone”, and “feeling energetically attacked”.
In their new analysis, the authors applied machine learning and classical statistical approaches to the same dataset to better understand the mediating factors shaping the relationship between adverse events and mental health outcomes in ayahuasca users. The survey included 10,836 participants, of whom 5,400 with complete data were included in the final analysis. Among these, 14.2% had a prior anxiety disorder and 19.7% a prior depressive disorder.. Although the Global Ayahuasca Survey reflects a large, diverse population of users, it was voluntary and administered potentially years after an individual’s ayahuasca experience, introducing self-selection and recall biases.
The researchers found that participants with a history of anxiety or depression, as well as those using ayahuasca in non-traditional settings, were more likely to report adverse mental states after use. Some “adverse effects” like visual distortions, however, were associated with significantly better mental health outcomes reported in the present. Adverse effects like “feeling down”, “feeling disconnected”, and “feeling energetically attacked” however, were associated with poorer mental health in participants in the longer term. The authors suggest that the context in which ayahuasca is used, as well as factors like age and mental health history, influence whether an individual experiences psychological benefits following an ayahuasca experience, and note that “adverse” effects of ayahuasca may be subjective.
Their findings appear to indicate that it would be more beneficial to use ayahuasca under the supervision of experienced users who can provide additional support to those with a history of depression, who may otherwise face a higher risk of negative outcomes. They propose that, while psychedelics are becoming increasingly medicalized, ayahuasca is most often consumed in group or community settings. Therefore, future studies should examine the effects of ayahuasca use in these real-life communal contexts.
Dr. José Carlos Bouso notes: "What stood out most to us was the significant difference in mental health outcomes between users who had supportive environments [during their use] and those who didn’t. This emphasizes the importance of a responsible and well-prepared setting for those seeking healing through ayahuasca."
The authors add:"Our study reveals that the post-ayahuasca mental states, traditionally seen as adverse, can contribute to improved mental health, especially in individuals with previous anxiety and depressive disorders. This suggests the need for a more nuanced understanding of these states as potentially beneficial experiences.”
Additional quotes:
On the Research Process: "The insights gathered from the Global Ayahuasca Survey (GAS) provided a deeper understanding of the complex relationship between ayahuasca use and mental health outcomes. It was particularly interesting to see how the setting, preparation, and integration practices play a pivotal role in shaping the overall experience" (Dr. José Carlos Bouso).
On the use of ayahuasca: "Ayahuasca use, when experienced in safe, supportive environments, may offer therapeutic benefits, particularly for individuals with a history of mood disorders, highlighting the importance of the ceremony's setting and the role of facilitators."
On the role of spirituality: "Our research also highlights that the spiritual significance of ayahuasca ceremonies plays a protective role, reducing adverse emotional states like anxiety, depression, and disconnection, thus contributing to overall mental health improvement.
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In your coverage please use this URL to provide access to the freely available article in PLOS Mental Health: https://plos.io/3YRG4DD
Citation: Andión Ó, Bouso JC, Sarris JJ, Tófoli LF, Opaleye ES, Perkins D (2025) A new insight into ayahuasca’s adverse effects: Reanalysis and perspectives on its mediating role in mental health from the Global Ayahuasca Survey (GAS). PLOS Ment Health 2(4): e0000097. https://doi.org/10.1371/journal.pmen.0000097
Author Countries: Australia, Brazil, Spain
Funding: The authors received no specific funding for this work.
Competing Interests:I have read the journal's policy and the authors of this manuscript have the following competing interests: DP and JJS hold equity in a commercial entity, Psychae Therapeutics, which is undertaking research with psychedelic compounds and are co-CEOs of the same organisation.
Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: DP and JJS hold equity in a commercial entity, Psychae Therapeutics, which is undertaking research with psychedelic compounds and are co-CEOs of the same organisation.
Machine learning brings new insights to cell’s role in addiction, relapse
University of Cincinnati, University of Houston collaborate on research published in Science Advances
Object recognition software is used by law enforcement to help identify suspects, by self-driving cars to navigate roadways and by many consumers to unlock their cell phones or pay for their morning coffee.
Now, researchers led by the University of Cincinnati’s Anna Kruyer and the University of Houston’s Demetrio Labate have applied object recognition technology to track changes in brain cell structure and provide new insights into how the brain responds to heroin use, withdrawal and relapse. The research was published April 30 in the journal Science Advances.
Study background
Kruyer’s lab focuses on relapse to heroin use, as many overdose deaths occur when people overestimate their capacity for drug use during relapse. The team has developed an animal model of relapse over the past seven years, studying interactions between brain cells and the reward center of the brain that orchestrates the relapse process.
“We want to understand the neurons that are involved and all of the different cells and molecules that can shape that activity,” said Kruyer, PhD, assistant professor in the Department of Pharmaceutical Sciences in UC’s James L. Winkle College of Pharmacy. “The idea would be if you can interfere with relapse, you can help someone stay clean.”
While neurons are a more commonly studied brain cell, Kruyer has focused on another cell called an astrocyte. Astrocytes have many functions, including metabolic support for neurons, providing molecules that neurons turn into neurotransmitters, and shielding or uncovering different receptors during synaptic activity.
“Astrocytes are a kind of protective cell that can restore synaptic homeostasis,” Kruyer said. “They are super dynamic relative to the synapse, and they’re moving toward and away from the synapse in real time in a way that can impact drug seeking. So if you prevent this reassociation with synapses during relapse, you can increase and prolong relapse."
Labate is an applied mathematician with expertise in harmonic analysis and machine learning.
“A central focus of my research is the development and application of mathematical techniques to uncover meaningful patterns in non-Euclidean data, such as the analysis of complex shapes,” said Labate, PhD, professor in the University of Houston Department of Mathematics. “The study of astrocytes provides an ideal setting for this type of investigation: these cells are highly heterogeneous, varying widely in size and shape, and are capable of dynamically remodeling their morphology in response to external stimuli.”
A new approach with machine learning
While animal model studies have produced results, Kruyer and her colleagues faced a barrier in that the techniques used could not be translated for human subjects. To work around this issue, they focused on an astrocyte protein that essentially acts as the cell’s skeleton.
“We thought if we could figure out a way to translate what we’re seeing at the synaptic level to changes in the cytoskeleton, maybe we could see if astrocytes are doing something critical during relapse in humans,” Kruyer said.
A team of mathematicians led by Labate trained object recognition machine learning models on hundreds of astrocyte cells until the technology could accurately detect an astrocyte within an image, similar to how object recognition software works.
“Machine learning techniques have been widely applied in the literature to image classification tasks, where the objective is to assign each cell to a specific category,” Labate explained. “In such contexts, machine learning is particularly powerful for identifying image-based cellular features that are difficult to capture using traditional geometric descriptors, yet serve as effective discriminators between classes.”
Once the program could identify astrocytes, the team trained it to analyze specific structures based on 15 different criteria, including astrocyte cytoskeletal density (similar to bone density), size, length versus sphericalness and number of smaller branches coming off of the main branch.
“You can think about this like if you gave a computer a bunch of images of street scenes, it would commonly see pedestrians, cars and buildings,” Kruyer said. “If you give a computer 1,000 images of astrocytes, there are things it would commonly see. This is the segmentation process whereby a computer can now start to make measurements of the different features of the astrocyte.”
Using all 15 measurements weighted by their importance in the computer’s precision to detect astrocytes, researchers developed a single metric to quantify the characteristics of each astrocyte.
“In previous work, I have utilized machine learning for both cell classification and segmentation problems,” Labate said. “In this paper, however, we address a more nuanced question: are there specific subpopulations of astroglia that exhibit more pronounced morphological changes compared to the rest? To investigate this, we introduced the concept of distance to compare the shape characteristics of individual astrocyte cells while accounting for the inherent heterogeneity within the population.”
Applying the model
After developing the machine learning model to identify astrocytes and report the new metric, the team looked at astrocytes specifically within an area of the brain called the nucleus accumbens (NAc) that is active during drug relapse.
The model was able to predict exactly where in the NAc an astrocyte came from based on its structure with 80% accuracy.
“This tells us that astrocyte structure varies by anatomy,” Kruyer said. “Astrocytes have been considered to be this homogenous type of cell, but this indicates to us that astrocyte structure varies significantly by location — perhaps the shape and the size have something to do with their function.”
Using animal models and the new knowledge gained from the computer models, the team found that astrocytes appear to shrink and become less malleable after exposure to heroin.
“These data suggest that heroin is doing something molecularly that makes astrocytes less able to respond to synaptic activity and maintain homeostasis,” Kruyer said.
“This paper exemplifies the strength of interdisciplinary collaboration, where innovative quantitative tools are developed or adapted to tackle complex biological questions,” added Labate. “The success of this research lies in the effective communication between disciplines and in our willingness to push the boundaries of traditional machine learning to address biologically meaningful and timely challenges.”
Next steps
Kruyer said she is most excited about the application of machine learning to a biological question, which eliminates human error and biases and makes the research more easily translatable from animal models to humans.
“We’re asking open-ended questions, and it’s giving us all of these really fine-grained detailed answers, and then what we do with that is up to us,” she said. “Human astrocytes are much larger, much more complex and way more abundant than in the animal models, so applying a tool like this is really cool to carry forward in humans.”
Moving forward, the team wants to learn more about the specific mechanisms of astrocytes in each region within the NAc and train new models using human tissue samples. Long term, the knowledge gained could help develop new treatments for addiction focused on restoring or replacing astrocytes to their functions prior to being exposed to heroin.
Additionally, the machine learning method Labate’s team developed can be adapted and applied to other types of cells with intricate structures.
“By enabling precise quantification and comparison of single-cell morphological features, this approach opens the door to the development of novel techniques for identifying cellular or molecular biomarkers that reflect biological processes, disease states or responses to therapeutic interventions,” he said. “More broadly, our work introduces a new quantitative framework for uncovering and validating fundamental mechanistic models underlying complex brain conditions, such as addiction to drugs of abuse.”
Other coauthors include Michaela Marini, Yabo Niu, Heng Zhao, Anish Mohan and Nathan Koorndyk. This work was supported by grants from the Simons Foundation (MPS-TSM783 00002738 to Labate) and the National Institutes of Health (DA054339 to Kruyer and NS007453 to Koorndyk). All authors declare no competing interests related to this manuscript.
