The role of GABAA receptors in hippocampus-dependent cognitive functions.

Tobacco use remains the leading preventable cause of death worldwide, while the rise of electronic nicotine products has sparked a new wave of initiation. The urgent need for scalable, multilevel tobacco-control interventions converges with the rapid advances in artificial intelligence (AI). This article reviews the most recent literature on integrating machine- and human expertise to enhance tobacco-cessation strategies within a multilevel framework. Recent advances in predictive analytics, large-language models (LLM), AI chatbots, and related tools create a framework to strengthen tobacco prevention. Predictive analytics merge electronic health records, behavioral surveys, genetics, and real-time sensor data to model the complex multilevel factors that influence quitting. LLMs instantly uncover informative features, revealing novel predictors that shape targeted interventions. AI-driven conversational agents deliver stage-specific counseling and medication guidance, with preliminary trials showing improved engagement and quit rates. Reinforcement learning personalizes messaging, rewards, and medication schedules to optimize outcomes, while natural-language processing of social media provides fine-grained sentiment data to assess policy impact. Realizing AI's potential to reduce tobacco's public-health burden requires interdisciplinary collaboration, equity-oriented design, external validation, and strong governance. These safeguards can enable scalable, adaptable, and culturally relevant smoking-cessation interventions and facilitate timely, effective tobacco-control policies.

Evidence for abstract spatial concept learning in young animals.

ABSTRACT Human emotions toward animals, perception of animal minds, and moral concern play crucial roles in shaping public support for wildlife management initiatives. This study uses Moral Typecasting Theory to explore how these psychological factors influence attitudes toward controlling two non-native species, feral cats (Felis catus) and European hedgehogs (Erinaceus europaeus), in the context of New Zealand’s Predator Free 2050 initiative. A sample of 395 New Zealand residents completed a survey assessing their emotional responses, perceptions of animal minds, and support for mitigation strategies concerning these species. Using latent profile analysis, we identified three distinct psychological segments for each animal – Advocates, Detractors, and Indifferents – characterized by varying levels of emotional engagement, perception of animal mind, resulting moral concern, and support for control measures. Advocates expressed strong positive emotions, high attribution of animal “experience” and moral concern, along with low support for lethal control; Detractors exhibited strong negative emotions, high attribution of animal “agency” associated with a lower attribution of moral concern, high perceived threat, and strong support for control; and Indifferents held moderate views across all dimensions. These profiles were significantly associated with demographic factors such as age, location, residency duration, and pet ownership. Our findings underscore the value of psychological segmentation in designing targeted communication and policy strategies to foster broader public engagement with conservation goals. We argue that tailoring interventions to the emotional and cognitive dispositions of different segments can enhance the legitimacy, effectiveness, and social sustainability of wildlife management initiatives.

Obesity-related health issues, including cognitive decline linked to hippocampal neurogenesis and neuroplasticity, are gaining more attention as obesity rates rise worldwide. Physical activity is recognized as a potent stimulator of neurotrophic factors. This study examined the impact of six weeks of treadmill training on hippocampal molecular pathways in adult female Zucker diabetic fatty (obese) and Zucker lean rats. Animals were assigned to either treadmill exercise (n = 10) or sedentary control (n = 10) groups. Endurance training (ET) markedly upregulated mRNA expression of brain-derived neurotrophic factor and its receptor. The PI3K/Akt pathway was upregulated only in the trained lean rats and downregulated in the trained obese group compared with sedentary controls. ET elicited divergent effects on neurotrophin-associated PLCγ/PKC/CAMKII signalling between lean and obese groups. Sedentary obese rats primarily utilized the PLCγ/PKC axis, while both trained groups (lean and obese) showed increased CAMKII expression, associated with enhanced synaptic plasticity and memory. Enhanced synaptophysin mRNA indicated improved synaptogenesis and plasticity following ET. Trained obese rats also exhibited reduced expression of the microglial pro-inflammatory marker Iba1, alongside increased markers of oligodendrocyte regeneration and neurofilament expression. Behavioral assessment via the passive avoidance test demonstrated improved learning and memory in trained obese animals. Collectively, these findings suggest that ET may mitigate obesity-induced hippocampal damage, exert neuroprotection, and enhance hippocampal function.

Animal learning can be analyzed on two timescales: task acquisition across training sessions and motivation fluctuations within training sessions. How do variations in motor and neurophysiologic activity relate to task performance over these timescales? Here, this question was examined in head-fixed mice performing a whisker-based sensory discrimination task. Male mice were trained for 12–14 daily sessions on a go/no-go task, each lasting ∼1 h to capture spontaneous performance fluctuations over minutes. Simultaneous to task performance, “nonperformance variables” were tracked, including wheel running, pupil size, eyelid aperture, and sensory cortical activity. First, motivation states were defined based on performance tendencies over minutes, leading to three state categories: persistent, disengaged, or attentive. Nonperformance variables were found to predict these states independent of task correctness. Then, when further parsing these states by the go/no-go outcomes of hit, miss, false alarm, or correct rejection, learning-like changes were detected in wheel running, eye movements, and brain activity. Thus, learning over days and motivation fluctuations over minutes form a continuum, as evidenced by changes in motor and physiologic activity variables not directly controlled by task contingencies, even during periods of suboptimal performance in well-trained subjects. These findings improve the understanding of performance variations and implicit learning, in addition to contributing a framework for the analysis of task performance indirectly from motor and neurophysiologic activity.

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [4–7]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [8–11]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

Standard animal learning studies minimize individual reward magnitudes to maximize the repetitions of reinforced behaviors. We investigated how reward magnitude influences initial learning across five behavioral paradigms in naïve mice. Especially large rewards could substantially improve learning efficiency through dissociable effects on within- and across-session learning and task engagement. The duration and magnitude of ventral striatal dopamine release scaled with reward sizes, and prolonged optogenetic enhancement of dopamine reward responses also reproduced much, but not all, of the benefits to learning produced by outsized rewards. These findings indicate that the reinforcement learning efficiency of animals has traditionally been underestimated and that dopamine signaling of rewards mediates task engagement in proportion to absolute reward magnitude.

The effects of alpha-tocopherol on seizure parameters, locomotor-cognitive functions, inflammatory response, oxidative stress response, histopathological changes, immunohistochemical parameters, and miRNA fold changes were investigated in rats with traumatic brain injury (TBI) and pentylenetetrazol (PTZ)-induced seizures. Sprague-Dawley male rats were randomly divided into three groups: Control (n = 8), TBI + PTZ (n = 10), and TBI + PTZ + tocopherol (n = 10). After inducing TBI in animals using the weight-drop method, increased post-injury seizure susceptibility was achieved by administering subconvulsive doses of PTZ. Saline was administered intraperitoneally to the control and TBI + PTZ groups for 6days, while 500mg/kg alpha-tocopherol was administered intraperitoneally to the TBI + PTZ + tocopherol group. Seizure intensity, seizure frequency, and total seizure duration were significantly reduced in the TBI + PTZ + tocopherol group compared to the TBI + PTZ group (p < 0.05). No significant adverse effects related to TBI and PTZ were observed in the animals' locomotor activity, anxiety-like behaviors, or learning and memory test outcomes. In the TBI + PTZ + tocopherol group, significant reductions were observed in inflammatory cytokine response, oxidative stress, and SUR1-TRPM4 channel activity compared to the TBI + PTZ group (p < 0.001). While degenerative and apoptotic neurons and the number of 8-OHdG-positive cells in the CA1 and dentate gyrus regions were limited in the TBI + PTZ + tocopherol group, downregulated miR-324-5p increased (p < 0.05). Alpha-tocopherol reduced the severity and duration of seizures, reduced oxidative stress and inflammation, and stabilized the thiol-disulfide balance. It also reduced degenerative cell structures and DNA damage in the cortex, hippocampus, and dentate gyrus. In conclusion, the findings of this study suggest that alpha-tocopherol is a potential neuroprotective agent that modulates early epileptogenic network instability in TBI and seizure susceptibility through multiple pathways, including oxidative stress, inflammation, and ion channel regulation.

Large Language Models (LLMs) are poised to fundamentally reshape entrepreneurial work, but it remains unclear whether this technology can support judgment-intensive entrepreneurial tasks. Prevailing skepticism holds that LLMs are inherently unreliable for such deep augmentation because, despite their language competence, they do not think. In contrast, we draw on Ludwig Wittgenstein and Alan Turing to advance a language-centered perspective on entrepreneurial work. Wittgenstein demystifies thought as linguistic activity and treats reasoning and understanding as linguistic abilities exercised in thinking. Extending this stance to the domain of machine intelligence, Turing grounds claims about intelligence in testable performances of language use. Together, they enable us to (1) conceptualize LLMs as an epistemic technology whose linguistic competence may suffice for the deep augmentation of entrepreneurship and (2) reorient research from skepticism toward fine-grained Turing tests of entrepreneurial work. We illustrate and support the language-centered perspective through two studies on crafting effective entrepreneurial narratives, a judgment-intensive task. Initially, we document that the LLM competently blends expert rhetorical strategies to create and refine narratives that effectively align with stakeholder needs. We then experimentally demonstrate that, when coupled with stakeholder-guided iterations, LLMs produce measurable improvements in narratives tailored to distinct stakeholder priorities. More broadly, our rethinking of entrepreneurial work through language-centered lenses helps theoretically support bold predictions about what entrepreneurs can accomplish with a nonhuman intelligence that has “only” mastered human language. • Challenges the philosophical underpinnings of skepticism about LLMs in entrepreneurial judgment • Positions LLMs as epistemic technologies for deep augmentation in entrepreneurship • Proposes a research program of fine-grained, task-specific Turing tests for entrepreneurial work • Demonstrates LLMs can craft and iteratively refine effective entrepreneurial narratives across diverse stakeholder priorities • Documents that LLMs help remove a key entry barrier to entrepreneurship

Insulin/Insulin-like growth factor 1 (IGF-1) signaling (IIS) is a pleiotropic signaling pathway that functions across tissues to coordinate phenotypic changes in response to nutrient status. Thus, the ubiquity of the IIS pathway hinders efforts to elucidate the mechanisms driving specific IIS-related phenotypes. Previous research in the nematode worm C. elegans has demonstrated that loss of function of the IIS transmembrane receptor (IR) ortholog, DAF-2, results in a doubled lifespan and enhanced learning and memory behaviors in young and aged animals. However, these findings are the result of reducing DAF-2 receptor function rather than modulating ligand-receptor interactions. In the current study, we aimed to dissect ligand-receptor interactions that may regulate associative behaviors apart from canonical IIS lifespan phenotypes in C. elegans. To this end, we performed targeted genetic screening of Insulin-like Peptides (ILPs) previously identified as DAF-2 antagonists to test their role in learning and memory phenotypes. We discovered that only a single uncharacterized ILP, INS-17, is required for learning and memory. We also demonstrate that INS-17 is sufficient to confer extended memory ability and can promote the maintenance of learning and memory with age. Additionally, we observe that INS-17 regulates associative behaviors independent of lifespan, uncoupling some IIS-mutant phenotypes. We find that regulation of the ins-17 genetic locus explains its unique requirement among ILPs for learning and memory behaviors. Finally, we found that INS-17 acts to signal a state of nutrient deprivation. This activity is required to properly process stimulus valence to promote advantageous behaviors. Our findings deepen the understanding of how IIS can regulate specific phenotypic outputs in response to changes in internal metabolic states.

We model human and animal learning by computing with high-dimensional vectors (e.g., D = 10,000). The architecture resembles traditional (von Neumann) computing with numbers, but the instructions refer to vectors and operate on them in superposition. The architecture includes a high-capacity memory for vectors, counterpart of the random-access memory (RAM) for numbers. The model's ability to learn from data reminds us of deep learning, but with an architecture closer to biology. The architecture agrees with an idea from psychology that human memory and learning involve a short-term working memory and a long-term data store. Neuroscience provides us with a model of the long-term memory, namely, the cortex of the cerebellum. With roots in psychology, biology, and traditional computing, a theory of computing with vectors can help us understand how brains compute. Application to learning by robots seems inevitable, but there is likely to be more, including language. Ultimately we want to compute with no more material and energy than brains use. To that end, we need a mathematical theory that agrees with psychology and biology and is suitable for nano-technology. We also need to exercise the theory in large-scale experiments. The analogy with traditional computing suggests that the architecture be programmable in terms of variables, values, and data structures, the very things that have made traditional computing ubiquitous and that seem worth learning from and emulating.

Alzheimer's disease (AD) has long been defined by amyloid-β plaques and hyperphosphorylated tau, yet disease-modifying therapies remain critically limited. Growing evidence reframes AD as a system-level failure driven by early dysregulation of synaptic, metabolic, and neuroimmune pathways, preceding overt protein aggregation and originating in selectively vulnerable circuits, including the locus coeruleus (LC)-hippocampal noradrenergic axis. This complexity underscores the need for therapeutic strategies that engage the disease at a network level, early in its trajectory. To this end, using a machine learning-based systems pharmacology framework for drug repurposing applied to human AD transcriptomic datasets, we identified terazosin (TZ) as a candidate predicted to reverse AD-associated molecular signatures. TZ is an FDA-approved α₁-adrenergic receptor antagonist and phosphoglycerate kinase-1 activator. It was administered chronically via the diet (0.5 mg/kg bw/day) to male and female TgF344-AD rats and wild-type littermates from 5 to 11 months of age, preceding overt pathology. Bulk hippocampal RNA sequencing revealed sex-specific transcriptional remodeling in transgenic rats, strongly conserved with human AD datasets. Male TgF344-AD rats exhibited suppression of synaptic and transcriptional maintenance pathways with concurrent activation of metabolic, proteostatic, extracellular matrix, and vascular stress responses; females showed suppression of survival and vascular structural signaling alongside heightened DAM-like immune activation, amyloid-associated stress, and cell death programs. TZ reversed these signatures in a sex-dependent manner: males showed enhanced immune surveillance and reduced proteostasis burden, while females showed reinforcement of synaptic, survival, and metabolic pathways. TgF344-AD rats displayed selective LC-derived hippocampal noradrenergic axonopathy without global neuronal loss. TZ preserved fiber integrity preferentially in females and partially reversed LC vulnerability-associated transcriptional signatures in both sexes. TZ also reduced amyloid-β plaque burden in both sexes, attenuated hyperphosphorylated tau exclusively in females, and induced microglial morphological shifts in males. Finally, TZ restored wild-type spatial learning in transgenic animals, with females appearing to derive the greater cognitive benefit. Together, these findings demonstrate that TZ induced systems-level reprogramming of AD-relevant molecular pathways and preserved vulnerable noradrenergic circuitry in a sex-dependent manner. Moreover, TZ rescued spatial cognition in transgenic rats, with cognitive gains seemingly more pronounced in females. These results support adrenergic-bioenergetic modulation as a translational strategy for early-stage AD and underscore the necessity of sex as a biological variable in disease-modifying treatment development.

Human color perception involves a tradeoff between our ability to discriminate millions of continuous hues and our reliance on a few discrete linguistic categories. Although some theories suggest these category boundaries are fixed perceptual anchors, others propose that judgments adapt dynamically to the statistical distribution of recent stimuli, known as the range effect. To test the stability of these boundaries, we adapted a fast-paced "match-to-sample" paradigm from animal learning into an immersive VR videogame. Participants used colored sabers to strike incoming cubes, matching saber color to the cube's stripe. We tested both the blue-green boundary (aligned with low-level cone mechanisms) and the pink-purple boundary (off-axis), using hue sets equated for discriminability. After establishing baseline category borders using psychometric functions, we shifted the range of tested colors toward one category endpoint to determine if the internal border remained stable or shifted with the stimulus distribution. Across four experiments, results consistently revealed a partial shift. Rather than remaining invariant, category borders shifted systematically in the direction of the stimulus range shift. Further manipulations demonstrated that this partial shift was unaffected by the proportion of responses and occurred even when using hues which don't contain a category boundary. These findings indicate that under rapid decision-making conditions, observers' judgments are strongly influenced by the statistical structure of the immediate stimulus set, with stable categorical anchors playing a more limited role. This suggests a limited role for linguistic color categories in active, matching-based tasks, where observers likely prioritize automatic statistical adaptation over fixed categorical distinctions.

Combined transcranial electrical stimulation (tES) and cognitive training (CT) for cognitive impairment: Evidence from clinical applications and basic research.

ABSTRACT A central concern in theological anthropology is the position of human beings within creation, to what extent and in what ways humans are unique and distinctive. This article examines relevant verses in Abū Manṣūr al-Māturīdī’s exegesis Ta’wilāt al-Qur’ān. What emerges is a cautious tendency to approach such matters from the perspective of human concerns, interests and needs, rather than in absolute ontological terms, which differs from anthropocentric tendencies prevalent in classical Islamic thought. This has relevance to current concerns in theological anthropology, like evolution, animal intelligence, and the possibility of extraterrestrials, which are arguably less challenging with al-Māturīdī’s perspectival approach.

Animal intelligence is not purely a product of abstract computation in the brain, but emerges from dynamic interactions between the nervous system and the body. New connectome datasets and musculoskeletal models now enable integrated, closed-loop simulations of the neural and biomechanical systems of the fruit flyDrosophila, an ideal model organism to investigate embodied intelligence. However, many biological parameters of the nervous system and the body, as well as how they interface, remain unknown. To fill such gaps, researchers are turning to deep reinforcement learning (DRL), a data-driven optimization framework, to create virtual animals that imitate the behavior of real animals. Here, we provide a cautionary tale about the interpretation of such models. We constructed a virtual chimera of two phylogenetically distant species: a connectome of theC. elegansnematode worm and a biomechanical model of the fly body. The worm connectome receives sensory information from the fly body, and an artificial neural network is trained with DRL to map worm motor neuron activations to the fly’s leg actuators. The resulting digital sphinx produces highly realistic fly walking—yet it is biologically meaningless. This exercise teaches us nothing about either animal and exposes a core peril of connectome-body models: behavioral fidelity is achievable without biological fidelity, making such models easy to overinterpret. Done carefully, virtual animals can be powerful partners to biological experiments, but only if their components and interfaces are grounded in biology.

Infection with the human immunodeficiency virus (HIV) causes human neuropsycho-logical disorders, such as apathy and hypokinesia, as well as deficits in motor skills, selective attention, and learning. Based on findings from multiple studies, similar signs have been repro-duced in animal models following intracerebral injections of HIV-infected human monocytes or monocyte-derived macrophages, or exposure to gp120, Tat, or Nef. These include learning defi-cits in Morris, radial arm, and Barnes mazes; impairments in novel object place and shape recog-nition; motor coordination deficits on stationary and mobile beams; and hypoactivity. Relative to non-transgenic controls, deficits in most tests have also been reproduced in transgenic mice or rats expressing HIV-1 or related proteins. There is evidence that corticosterone contributes to these behavioral abnormalities, which may have implications for treating AIDS dementia com-plex, given its ability to exacerbate the neurotoxic effects of gp120 in tissue cultures. Possible mechanisms include corticosterone-induced worsening of lipid peroxidation, inhibition of aspar-tate uptake, increased calcium mobilization, and reduced ATP levels.

The swift proliferation of Artificial Intelligence (AI) and algorithmic technologies in the criminal justice system and legal profession is apparent worldwide. Machine learning algorithms are currently impacting sentencing determinations in multiple jurisdictions globally. Artificial intelligence, in contrast to the organic intelligence of humans and animals, possesses the ability to learn, adapt, and evolve through the assimilation of knowledge and new information. AI software can independently learn and enhance its performance based on the data it acquires. These algorithms can identify trends, elaborate on them, and discover more effective methods for executing specific activities via automation. Human fallibility necessitates the integration of AI as an essential instrument in judicial decision-making, especially in criminal sentencing, thereby aiding judges in achieving more precise and efficient outcomes, which permits them to concentrate on other matters. This paper assesses the application of artificial intelligence in criminal sentencing within the judicial system from a comparative standpoint. A mixed-method approach was utilised, integrating qualitative and quantitative techniques for data collecting, encompassing in-person interviews and surveys. The research demonstrated that AI systems can swiftly analyse extensive amounts of legal language, facilitating tasks such as transcription, document summarisation, and legal research. AI employs diverse algorithms to address a multitude of problems through pattern recognition and the execution of precise instructions. Through the analysis of huge datasets, AI can deliver impartial recommendations, resulting in more uniform sentence outcomes for analogous situations. Artificial intelligence can evaluate an offender's likelihood of recidivism by analysing variables such as age and educational background, thereby aiding courts in making educated sentence decisions. The study indicated that the risks and obstacles linked to AI in sentencing encompass prejudice amplification, potential coercion of judges to conform to AI suggestions, and ethical issues related to transparency and human rights. To alleviate these hazards, transparency mandates are crucial for enabling judges to comprehend the elements evaluated by AI systems. Stringent data-quality criteria must be established to avert the recurrence of inequitable sentencing practices. Proactive supervision and regulation of AI are essential, accompanied by rigorous legislation overseeing the design and development of algorithms. Consistent auditing, targeted AI training, and instruction for judges and legal practitioners are essential for the proper integration of AI in the criminal justice system..

The objective of this study was to compare the cash flow of lactating Holstein cows randomized to health monitoring strategies that relied exclusively on automated health alerts or visual observation for selecting cows for clinical examination from 3 to 21 DIM. Data from a randomized controlled trial were used to estimate total cash flow per cow and per slot up to 100 DIM. We expected that greater milk yield in early lactation for the group monitored with automation would result in greater cash flow than for the group including visual observation despite greater health monitoring and treatment costs. Lactating Holstein cows (n = 1,192) fitted with a neck-attached automated rumination time and physical activity monitoring system (Sensehub, Merck Animal Health) and that had milk weights collected automatically at every milking (MM27BC, DeLaval) were randomly assigned to the visual observation (VO = 594) or automated health monitoring (AHM = 598) treatment. Cows were selected for clinical examination based on automated health alerts (i.e., health index score <86 arbitrary units, daily rumination time <250 min, and reduction in daily milk yield >20%) in the AHM treatment and exclusively based on visual observation of clinical signs of disease in the VO treatment. Health, milk yield, and herd exit outcomes, and prices for inputs and outputs were collected through 100 DIM. Cash flow per cow and per slot (unit of space occupied by a cow at a dairy) including milk income over feed costs, health monitoring and management costs, treatment costs, and replacement costs were estimated and compared using deterministic analysis with linear models and stochastic analysis. Regardless of the method of estimation for cows that remained in the herd, cows that exited the herd, or for all cows combined, cash flow differences from the deterministic analysis generally favored the AHM treatment. Likewise, the stochastic simulations supported the hypothesis that most of the time, the AHM treatment would result in positive cash flow compared with the VO treatment. Notably, the magnitude of the differences between treatment groups varied substantially based on the effects of treatments on herd performance outcomes, the herd exit dynamics, prices for influential inputs and outputs, and the methods used to estimate cash flow. Implementing a fresh cow health monitoring program that relied exclusively on automated monitoring system alerts to select cows for clinical examinations was economically beneficial compared with a program that relied exclusively on visual observation to select cows for clinical examinations.