Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity

As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional approaches in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. Throughout the history of researching scientific problems, scientists have continuously formed new paradigms facilitated by advances in data, algorithms, and computational power. To better tackle unresolved problems, especially those of organised complexity, a novel paradigm is necessitated. While recognising that the strengths of new paradigms have expanded the scope of resolvable scientific problems, we aware that the continued advancement of data, algorithms, and computational power alone is hardly to bring a new paradigm. We posit that the integration of paradigms, which capitalises on the strengths of each, represents a promising approach. Specifically, we focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it. SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours. NGS extends beyond the capabilities of traditional mathematical modelling simulations and agent-based modelling simulations, and therefore, positions itself as a potential solution to problems of organised complexity in complex systems

Transform then Explore: a Simple and Effective Technique for Exploratory Combinatorial Optimization with Reinforcement Learning

Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs) over graphs. Recent years, reinforcement learning (RL) based models have emerged as a promising direction, which treat the COPs solving as a heuristic learning problem. However, current finite-horizon-MDP based RL models have inherent limitations. They are not allowed to explore adquately for improving solutions at test time, which may be necessary given the complexity of NP-hard optimization tasks. Some recent attempts solve this issue by focusing on reward design and state feature engineering, which are tedious and ad-hoc. In this work, we instead propose a much simpler but more effective technique, named gauge transformation (GT). The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test. Morever, GT is very simple, which can be implemented with less than 10 lines of Python codes, and can be applied to a vast majority of RL models. Experimentally, we show that traditional RL models with GT technique produce the state-of-the-art performances on the MaxCut problem. Furthermore, since GT is independent of any RL models, it can be seamlessly integrated into various RL frameworks, paving the way of these models for more effective explorations in the solving of general COPs

Association of a Healthy Lifestyle With All-Cause and Cause-Specific Mortality Among Individuals With Probable Sarcopenia: Population-Based Cohort Study

BackgroundIndividuals with probable sarcopenia have shown excess mortality, yet no specific treatment regimen has been established. While lifestyle factors improve health and longevity in general populations, their role in probable patients with sarcopenia remains unclear due to differing lifestyle patterns. Clarifying this could inform strategies to address this unmet need.ObjectiveWe aim to quantify the impact of a healthy lifestyle on all-cause and cause-specific mortality in probable sarcopenic populations using a large-scale prospective cohort study.MethodsParticipants were selected from the UK Biobank, aged 40-69 years, during 2006-2010. Probable sarcopenia was identified according to EWGSOP2 (European Working Group on Sarcopenia in Older People 2) criteria, resulting in 20,654 participants being included in this study. Death dates and underlying causes were obtained from the National Health Service Information Center. Cox proportional hazard models and population-attributable risk were used to assess the associations between healthy lifestyle factors and premature mortality risk.ResultsA total of 20,654 individuals with probable sarcopenia were included in this study. The median age of the population was 62.0 (IQR 56.0-66.0) years, and 60.6% (n=12,528) were women. During a median follow-up duration of 11.5 (IQR 10.8-12.3) years, 2447 participants died. All healthy lifestyle factors, including nonsmoking (P<.001), moderate alcohol intake (P<.001), regular physical activity (P<.001), a healthy diet (P=.01), limited television-watching time (P<.001), adequate sleep duration (P=.001), and strong social connections (P<.001), were independently associated with lower mortality risk. To evaluate the cumulative associations between modifiable lifestyle factors and mortality outcomes (all-cause and cause-specific) among patients with probable sarcopenia, we developed a healthy lifestyle index. Participants were assigned one point per adherence to each optimal lifestyle factor. Compared with individuals with 0-2 healthy lifestyle scores, hazard ratios of all-cause mortality for those with 3 to 6-7 factors were 0.67 (95% CI 0.59-0.76), 0.51 (95% CI 0.45-0.57), 0.43 (95% CI 0.38-0.49), and 0.33 (95% CI 0.29-0.39), respectively (P for trend <.001). There was also a dose-response relationship between the number of healthy lifestyle factors and mortality from cancer, cardiovascular disease, respiratory disease, digestive disease, and other causes (all P for trend<.001). Population-attributable risk analysis indicated that 25.7% (95% CI 22%-29%) of deaths were attributable to a poor lifestyle (scoring 0-5).ConclusionsA healthy lifestyle is associated with a lower risk of all-cause mortality and mortality due to cancer, cardiovascular disease, respiratory disease, and digestive disease among individuals with probable sarcopenia. Adopting a healthy lifestyle (scoring 6-7) could prevent 25.7% of deaths in this population

Smoking, alcohol and risk of sarcopenia: a Mendelian randomisation study.

OBJECTIVE: Observational studies have found that cigarette smoking increased the prevalence and incidence of sarcopenia, whereas alcohol consumption appeared to decrease the risk. These findings, however, may be susceptible to either confounding bias or reverse causation. We conducted a Mendelian randomisation (MR) study to appraise the causal relation of cigarette smoking and alcohol consumption to the risk of sarcopenia. METHODS: Genetic instruments associated with cigarette smoking (cigarettes per day) and alcohol consumption (drinks per week) were retrieved from the publicly available genome-wide association data. Individual-level, electronic medical record-linked data on sarcopenia, grip strength and appendicular lean mass were obtained from the UK Biobank. We performed two-sample univariable and multivariable MR analyses to examine the relation of genetically determined cigarette smoking and alcohol consumption to the risk of sarcopenia and its indices. RESULTS: One SD increase of genetically determined cigarette smoking was associated with an increased risk of sarcopenia (OR=2.51, 95% CI: 1.26 to 5.01, p=0.001), decreased grip strength (β=-0.63 kg, 95% CI: -1.13 to -0.13, p=0.01) and less appendicular lean mass (β=-0.22 kg, 95% CI: -0.44 to -0.01, p=0.04). Although one SD increase of genetically determined alcohol consumption was associated with decreased grip strength (β=-1.15 kg, 95% CI: -2.09 to -0.10, p=0.02), no statistically significant causal association was observed between genetically determined alcohol consumption and either sarcopenia (OR=0.96, 95% CI: 0.35 to 2.62, p=0.94) or appendicular lean mass (β=-0.23 kg, 95% CI: -0.91 to 0.45, p=0.51). CONCLUSIONS: Our findings showed that genetically determined cigarette smoking, but not alcohol consumption, was causally associated with the risk of sarcopenia

CACNA2D3 Enhances the Chemosensitivity of Esophageal Squamous Cell Carcinoma to Cisplatin via Inducing Ca2+-Mediated Apoptosis and Suppressing PI3K/Akt Pathways

Resistance to platinum-based combination chemotherapy is the main cause of poor prognosis in patients with advanced esophageal squamous cell carcinoma (ESCC). Previously, we showed that CACNA2D3 (voltage-dependent subunit alpha 2 delta 3 of a calcium channel complex) was significantly downregulated and functioned as a tumor suppressor in ESCC, but its role in the chemosensitivity of ESCC to cisplatin remained unknown. Here, we found that the expression of CACNA2D3 was significantly associated with poor platinum response in ESCC patients from the Gene Expression Omnibus database. Overexpression of CACNA2D3 increased sensitivity to cisplatin in ESCC in vitro, whereas knockdown of CACNA2D3 increased cisplatin resistance. CACNA2D3 promoted cisplatin-induced apoptosis by modulating intracellular Ca2+ stores. In vivo experiments further showed that overexpression of CACNA2D3 enhanced cisplatin anti-tumor effects in a xenograft mouse model. CACNA2D3 overexpression also resulted in the attenuation of PI3K and Akt phosphorylation. Treatment with the PI3K/Akt inhibitor LY294002 restored the chemosensitivity of CACAN2D3-knockdown cells to cisplatin. In conclusion, the results of the current study indicate that CACAN2D3 enhances the chemosensitivity of ESCC to cisplatin via inducing Ca2+-mediated apoptosis and suppressing PI3K/Akt pathways. Therefore, regulating the expression of CACNA2D3 is a potential new strategy to increase the efficacy of cisplatin in ESCC patients

Gut-joint axis in knee synovitis: gut fungal dysbiosis and altered fungi–bacteria correlation network identified in a community-based study

Objectives: Knee synovitis is a highly prevalent and potentially curable condition for knee pain; however, its pathogenesis remains unclear. We sought to assess the associations of the gut fungal microbiota and the fungi–bacteria correlation network with knee synovitis. Methods: Participants were derived from a community-based cross-sectional study. We performed an ultrasound examination of both knees. A knee was defined as having synovitis if its synovium was ≥4 mm and/or Power Doppler (PD) signal was within the knee synovium area (PD synovitis). We collected faecal specimens from each participant and assessed gut fungal and bacterial microbiota using internal transcribed spacer 2 and shotgun metagenomic sequencing. We examined the relation of α-diversity, β-diversity, the relative abundance of taxa and the interkingdom correlations to knee synovitis. Results: Among 977 participants (mean age: 63.2 years; women: 58.8%), 191 (19.5%) had knee synovitis. β-diversity of the gut fungal microbiota, but not α-diversity, was significantly associated with prevalent knee synovitis. The fungal genus Schizophyllum was inversely correlated with the prevalence and activity (ie, control, synovitis without PD signal and PD synovitis) of knee synovitis. Compared with those without synovitis, the fungi–bacteria correlation network in patients with knee synovitis was smaller (nodes: 93 vs 153; edges: 107 vs 244), and the average number of neighbours was fewer (2.3 vs 3.2). Conclusion: Alterations of gut fungal microbiota and the fungi–bacteria correlation network are associated with knee synovitis. These novel findings may help understand the mechanisms of the gut-joint axis in knee synovitis and suggest potential targets for future treatment

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

Intelligent Decision-Making (IDM) is a cornerstone of artificial intelligence (AI), designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation Models (FMs) have become pivotal in science and industry, trans- forming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM’s evolution, advanced paradigms with FMs, and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.<br/