Researchers studying 5556 participants in a Chinese endoscopy-screened cohort found that genetic risk and lifestyle independently and jointly influenced upper gastrointestinal cancer risk. The study identified a potential new gastric cancer susceptibility locus at 6p12.1 from genome-wide association study and showed that adding polygenic risk scores to conventional risk models improved risk stratification. A healthy lifestyle reduced risk across genetic risk groups, with greater benefit among those at high genetic risk.

Lanthanide ratiometric nanothermometers often deviate from Boltzmann statistics, so calibration and sensitivity become unpredictable. Researchers developed a population dynamics framework that defines the onset temperature and thermal coupling window and yields a practical rule: the nearest lower level must lie beyond twice the interlevel gap. A splitting factor links sensitivity with chemical bonding. Using dual thermally coupled pairs, ultrathin flexible patches achieve real time temperature monitoring with up to 6.17 % K^-1 relative sensitivity.

A retrospective study of 17 drug-resistant epilepsy patients found that stereo-electroencephalography-guided radiofrequency thermocoagulation (RF-TC) rapidly altered brain network connectivity after treatment. Reductions in alpha-band connectivity within epileptogenic regions were linked to clinical outcomes, suggesting that short resting-state recordings may help predict treatment response. Findings support RF-TC as a network-modulating therapy rather than a purely local lesioning procedure and highlight its potential role in guiding more personalized epilepsy care.

Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.

Researchers have developed "MetaRL," a meta reinforcement learning model that provides near-optimal, individualized investment strategies for goals-based wealth management, i.e., the portfolio composition and the decision to take a goal or not. Pre-trained on thousands of scenarios, MetaRL achieves 97.8% of optimal utility while operating over 100 times faster than traditional methods. The model delivers immediate "zero-shot" solutions in milliseconds and remains robust against changing market regimes and stochastic inflation, enabling high-throughput, sophisticated financial planning for a broad range of investors.

In recent years, dengue transmission in Vietnam has transitioned from predictable seasonality to climate-driven instability due to rising temperatures, urbanization, and hydrological extremes, among other factors. Traditional early warning systems based on case averages are becoming increasingly unreliable because of climate non-stationarity, resulting in both false alarms and missed warnings. This perspective summarizes available research on temperature, drought, rainfall extremes, diurnal temperature range, heatwaves, and El Niño anomalies, highlighting biologically-based thresholds that have been observed to precede outbreaks. We advocate for a pragmatic, hybrid early warning system that combines probabilistic national models with operationally simple meteorological triggers adapted to localized conditions. Integrating One Health factors, such as water storage practices, urban livestock habitats, and climate-sensitive vector control technologies, can facilitate optimal responses. This climate-informed, trigger-based approach helps to provide a realistic dengue control and prevention strategy that navigates low-cost and “no regrets” interventions in the face of accelerated environmental change.