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Artificial Intelligence as the Fourth Decentering Revolution: From Cosmic, Biological, and Psychological Displacement to Cognitive Decentering
Get PDFArtificial intelligence (AI) is commonly framed as a technological force transforming economies, labor markets, scientific discovery, and social interaction. While these impacts are profound, they represent only the surface of a deeper historical and philosophical shift. We argue that AI constitutes a fourth major decentering revolution in human self-understanding. The Copernican revolution displaced humans from the center of the universe, the Darwinian revolution displaced humans from the pinnacle of biological life, the Freudian revolution displaced the conscious ego from the throne of the mind, and today AI initiates a cognitive decentering revolution, challenging the long-held assumption that humans occupy a unique and unassailable position at the apex of intelligence. By situating AI within historical, philosophical, and technological contexts, incorporating detailed case studies, illustrative examples, and policy analysis, this paper provides a comprehensive account of cognitive decentering and its societal implications
EMBO Press co-evolves with molecular ecology and evolutionary biology
Get PDFMolecular ecology and evolution are central to understanding how biological systems function, interact, and diversify. A special issue of this journal reflects the growing synergy of molecular, genomic and cell biology with ecological and evolutionary reasoning. Accordingly, EMBO Press is recalibrating its editorial practices to better support studies embedded in ecological and evolutionary contexts
Artificial intelligence for personalized multiple micronutrient supplementation in maternal health
Get PDFMaternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long‐term health risks for mothers and offspring. Multiple micronutrient supplementation (MMS) during pregnancy has demonstrated benefits, including reduced risks of low birth weight, small‐for‐gestational‐age births, and neonatal mortality, when compared with standard iron–folic acid supplementation. Current MMS strategies, however, often follow a standard MMS, overlooking variations in nutritional status, health profiles, and context. Advances in artificial intelligence (AI), particularly deep learning and natural language processing, provide opportunities to strengthen maternal nutrition programs by integrating diverse data sources. Rather than promising fully individualized recommendations, AI could help stratify women by risk of insufficiencies or deficiencies, highlight groups most likely to benefit from additional support, and inform the design of more responsive supplementation strategies during preconception and pregnancy. We outline a conceptual model in which multimodal health data—including electronic health records (EHRs), wearable sensor outputs, nutrition and fertility app logs, genomic markers, and sociodemographic information—are aggregated and analyzed by AI systems to inform personalized MMS plans. The framework introduces the concept of a “nutritional digital twin,” a virtual profile of the patient's nutritional and metabolic state. This digital twin can simulate micronutrient needs and predict maternal–fetal outcomes under different supplementation scenarios, enabling clinicians to test scenario‐based options (e.g. standard MMS ± targeted add‐ons) for individuals. We describe how deep learning models can identify complex patterns (e.g. diet–genome interactions or behavioral trends) while natural language processing (NLP) algorithms extract clinically relevant insights from unstructured data (such as medical notes or patient queries). In addition, we discuss the role of digital maternal health tools, such as mobile apps and wearable trackers, in supplying real‐time data to the AI models and in engaging women to improve adherence to supplementation regimens. Harnessing AI for MMS could transform maternal nutrition care in both high‐ and low‐resource settings. In high‐income contexts, rich data (comprehensive EHRs, genetic tests, continuous monitoring devices) could feed advanced predictive models to support risk‐stratified care with protocolized supplementation options, under clinical oversight. In low‐ and middle‐income countries, where maternal undernutrition and micronutrient gaps are most prevalent, AI‐driven approaches can help stratify risk groups and optimize limited resources. Ubiquitous mobile phone access and digital health tools in many such settings provide avenues for data collection and intervention delivery. We highlight examples where machine learning on population data revealed “hidden hunger” patterns and key predictors of low supplement uptake (e.g. low education, minimal antenatal visits)—insights that policymakers can use to target nutrition programs. A nutritional digital twin could further allow scenario‐testing (e.g. predicting the impact of adding a vitamin D supplement for a specific patient) before clinical decisions are made. To realize this vision, the key concerns are ethics, credibility, and fairness. Ethical frameworks must guide development so that sensitive reproductive health data are protected and clinician oversight remains central. The credibility of AI‐generated recommendations depends on transparency about the assumptions used to translate nutritional and health data into supplement type and dose, and on prospective validation against maternal and neonatal outcomes. This requires a continuous feedback loop in which recommendations are tested in real‐world settings and recalibrated using outcomes data, ensuring that the system learns from observed benefits and harms, rather than relying solely on theoretical modeling. Fairness demands that training data sets represent diverse populations and that solutions are tailored to local contexts to reduce bias and avoid widening disparities. Critically, the approach must be fed by data streams that extend beyond initial demographics and clinical baselines to include biomarkers, adherence patterns, and pregnancy outcomes, so that the models can be refined and dosing rules adjusted over time. If these safeguards are embedded, AI‐enhanced personalized MMS can move beyond proof of concept towards a credible, equitable, and empirically grounded contribution to global maternal health. AI‐driven personalized nutrition support represents a frontier in obstetric care. By combining clinical knowledge with data‐driven intelligence, we can move beyond generalized prenatal supplements towards precision maternal nutrition. The integration of deep learning models and digital health innovations into antenatal care pathways has the potential to better nourish pregnancies, save lives, and ensure healthier futures for mothers and children worldwide
Surgical Treatment of Idiopathic spinal cord herniation: A Case Report under Neuromonitoring and Meta-analysis of 211 reviewed Cases
Get PDFBackgroundIdiopathic spinal cord herniation (ISCH) is a rare condition caused by a ventral or ventrolateral dural defect. Two surgical strategies are used: non-closure (Group I), consisting of reduction and adhesiolysis with or without enlargement of the defect, and closure (Group II), involving reduction followed by direct or indirect dural repair. This study compared these approaches.MethodsWe reported a case of ISCH treated under neuromonitoring. We conducted a systematic review and meta-analysis including surgically treated cases confirmed by MRI or CT myelography, excluding traumatic, iatrogenic, and discogenic etiologies. Outcomes included neurological status at final follow-up, recurrence, and surgical complications.ResultsA 50-year-old patient with progressive Brown-Séquard syndrome underwent surgery; neuromonitoring deterioration during attempted closure prompted conversion to non-closure. At the final follow-up, a clinical improvement without recurrence or complications was observed. Through the review, we identified 211 patients, including our case (mean age 50.99 ± 13.25 years; 58.7% female). Median follow-up was 24.16 months. In Group I, 53 improved, 4 were unchanged, and 2 worsened; in Group II, 114 improved, 27 were unchanged, and 9 worsened. Non-closure was associated with higher odds of improvement in the unadjusted analysis (POR 2.74, 95% CI 1.09-6.90, p = 0.032), but this association attenuated after adjustment (adjusted POR 2.53, 95% CI 0.69-9.31, p = 0.16). Complication rates were 3.38% vs 8.00% (OR 0.40, 95% CI 0.04-1.90); recurrence occurred once in each Group.ConclusionsBoth strategies are comparable. The non-closure could be a better choice, since it requires less manipulation. Intraoperative neuromonitoring is a valuable decision-making tool in ISCH surgery
Subtyping Alzheimer’s disease and Parkinson’s disease using longitudinal electronic health records
Get PDFNeurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) are clinically heterogeneous, hampering the success of nonselective treatment strategies. Here we apply a transformer-based unsupervised clustering framework to longitudinal electronic health record data from over 100,000 patients across two UK cohorts, Clinical Practice Research Datalink Aurum and UK Biobank, to identify, validate and characterize subtypes of AD and PD. We uncover five reproducible subtypes for each condition, characterized by distinct comorbidity patterns, symptom trajectories, outcomes and genetic profiles. These include a high-mortality AD subtype with motor and cardiovascular features, and a genetically susceptible but clinically resilient PD subtype. We also identify metabolic-inflammatory and vascular-psychiatric phenotypes shared across AD and PD, suggesting cross-disease mechanisms. By integrating routinely collected electronic health record data with genetic analyses, our study provides a scalable framework for early, biologically informed subtyping, laying the groundwork for future targeted interventions in neurodegenerative diseases
Symptom flares in endometriosis: burden, self-management and barriers to care in a cross-sectional survey
Get PDFObjective: To explore the characteristics of symptom flares, individual experiences and behaviours during flares in people with endometriosis.Design: Online questionnaire shared on patient support sites.Setting: People with a confirmed or working diagnosis of endometriosis (a working diagnosis is given by clinicians based on symptoms/history, individuals may or may not go on to have further imaging/surgical investigations).Population or Sample: A total of 236 responses collected. Methods: Descriptive and comparative analysis of quantitative data, and thematic analysis of qualitative data.Main Outcome Measures: The characteristics, triggers, treatments and strategies for symptom flares together with perceived predictability and self-efficacy in relation to flares, healthcare access during flare, advice received and overall endometriosis-related quality-of-life.Results: We identified a wide variation in the characteristics and treatments/strategies. 31.2% stated that they were “not at all” confident coping with long flares, and around 1/3 of participants found flares “not at all” predictable. Only 35.3% reported receiving advice from a healthcare provider about flares. We developed 5 themes to suggest why participants did not contact healthcare providers: ‘what can they do?’, ‘I can cope, it will end’, ‘broken healthcare system’, ‘perceived dismissal and gaslighting’ and ‘symptoms stop me’.Conclusions: Flares have a large impact on quality-of-life and are clinically very important. Individuals do not commonly receive advice from healthcare providers or contact healthcare providers during a flare. More research, in a more diverse sample, is needed to identify mechanisms underlying flares, as well as developing and disseminating management tools to prevent, manage and treat flares
Training efficient agents for long-term decision making
Get PDFReinforcement learning has ventured from tabletop simulators to real robots and open-world games, but today’s agents still learn with prohibitively low sample efficiency, ignore the priors encoded in foundation models, and forget most of what they have seen after a few hundred steps. This thesis pursues a unifying agenda—efficiently training efficient decision-making agents—through three successive contributions.Chapter 1 demonstrates that sample efficiency can be substantially improved by re-weighting experience toward the transitions that are most informative. An ensemble-based uncertainty criterion selectively upsamples those rare interactions that clarify causal structure, enabling offline reinforcement learning to achieve safe, performant policies with far fewer gradient updates than uniform replay.Stronger supervision is possible even when no new interaction data are collected, provided we can import structure learned elsewhere. Chapter 2 investigates this idea by tapping the internal representations of large generative vision models. Text-to-image diffusion backbones, although trained for synthesis rather than control, accumulate multi-scale spatial and semantic cues that are difficult to rediscover from scratch in a robotics dataset. By freezing these backbones and projecting their multi-layer activations into a control-friendly embedding—what we term Stable Control Representations (SCRs)—an agent starts with a rich inductive prior over object geometry and language grounding. In manipulation and open-vocabulary navigation tasks, SCRs cut the number of gradient steps needed to reach a given return by up to an order of magnitude and consistently outperform contrastively trained encoders, all without generating a single additional pixel. This result shows that re-using pretrained knowledge can convert computationally expensive exploration into cheap representation reuse, markedly improving sample efficiency.While these chapters focus on learning efficiently, deployed agents must also act efficiently by leveraging context that spans hours or days. Chapter 3 introduces Memo, a transformer policy that interleaves periodic summary tokens with streaming observations so memory capacity grows gently with task length. To measure such long-term reasoning, Chapter 4 contributes FindingDory, a procedurally extendable benchmark family whose 60 tasks probe how well embodied agents store and retrieve experience.Together, these works chart a coherent path toward agents that learn quickly, inherit rich priors, and remember what matters, moving a step closer to truly lifelong, self-improving intelligence
Jellyfish galaxies in magnetic fields: insights from numerical simulations
Get PDFJellyfish galaxies provide direct evidence of ram pressure stripping in cluster environments. We investigate the role of magnetic fields in the formation of jellyfish galaxies with a multiphase interstellar medium (ISM) using radiation magnetohydrodynamic simulations. We impose magnetized (magnetohydrodynamic; MHD) and nonmagnetized (hydrodynamic; HD) winds on the gas-rich dwarf galaxies containing the magnetized or nonmagnetized ISM. The MHD winds strip the disk gas more effectively than the HD winds because of the magnetic force acting against the local density gradient, which results in remarkably different ram pressure stripped features. The magnetic fields induced by the MHD winds generate a strong magnetic pressure, which forms smoothed disks and tail gas features. Since the stripped ISM in MHD wind cases travels while being nearly isolated from the intracluster medium (ICM), the stripped ISM mostly forms stars within 20 kpc of the galactic disks. In contrast, nonmagnetized winds facilitate the efficient mixing of the stripped ISM with the ICM, resulting in the formation of abundant warm clouds that cool and collapse in the distant (∼50–100 kpc) tails at times of a few hundred Myr. Consequently, distant tail star formation occurs only in the HD wind runs. Finally, despite the different tail features, the star formation rates in the disk remain similar owing to the interplay between the increased gas stripping and the gas density increase in the disks of the MHD wind runs. These results suggest that the magnetized ICM may have a significant influence on jellyfish galaxies, whereas the magnetized ISM play a minor role
Phospholipid-Based Delivery System Optimizes the Solubility and Systemic Exposure of Palmitoylethanolamide and Supports Clinical Benefits in Chronic Neuropathic Low Back Pain
Get PDFFamily Member and Healthcare Provider Perceptions of Factors Influencing Undernutrition Among Infants and Young Children in South Asia: A Systematic Review of Qualitative Studies
Get PDFBackground: Undernutrition among infants and young children in South Asia remains a major public health concern, contributing to high rates of morbidity and mortality. While quantitative systematic reviews have identified various risk factors for undernutrition, no review has focused on qualitative studies. This study aims to review published literature on family member and healthcare provider perceptions about influences on undernutrition among infants and young children in South Asia. Methods: We searched for qualitative research articles published from 2000 to 2026 in the PubMed, Scopus and CINAHL databases, and used the Critical Appraisal Skills Program (CASP) tool to assess the quality of selected articles. Selected articles were analyzed thematically. The PROSPERO registration number is CRD42022385382. Results: After screening 201 research articles, 19 articles were selected for inclusion in this review. Perceived influences of undernutrition among children were categorized into individual, socio-cultural, economic, environmental and system factors. Interconnected influences included maternal illness, single motherhood, mothers’ knowledge and awareness, convenience of providing low-quality ready-made and junk food, spiritual beliefs and superstition, violence against women, financial constraints in a context of rising food prices and seasonal impacts on food production, and physical accessibility of healthcare services. Conclusions: This review emphasizes the complex interplay of influences on undernutrition among young children in South Asia. Potential interventions must be culturally tailored and gender-sensitive, with key strategies including nutrition education, community-based support, maternal health improvements, and policies addressing food insecurity and healthcare accessibility