Analytic elasticity solution of bi-modulus beams under combined loads

A unified stress function for bi-modulus beams is proposed based on its mechanic sense on the boundary of beams. Elasticity solutions of stress and displacement for bi-modulus beams under combined loads are derived. The example analysis shows that the maximum tensile stress using the same elastic modulus theory is underestimated if the tensile elastic modulus is larger than the compressive elastic modulus. Otherwise, the maximum compressive stress is underestimated. The maximum tensile stress using the material mechanics solution is underestimated when the tensile elastic modulus is larger than the compressive elastic modulus to a certain extent. The error of stress using the material mechanics theory decreases as the span-to-height ratio of beams increases, which is apparent when L/h ≤ 5. The error also varies with the distributed load patterns.

Experimental models for cancer brain metastasis

Brain metastases are a leading cause of cancer-related mortality.However,progress in their treatment has been limited over the past decade,due to an incomplete understanding of the underlying biological mechanisms.Employing accurate in vitro and in vivo models to recapitulate the complexities of brain metastasis offers the most promising approach to unravel the intricate cellular and physiological processes involved.Here,we present a comprehensive review of the currently accessible models for studying brain metastasis.We introduce a diverse array of in vitro and in vivo models,including cultured cells using the Transwell system,organoids,microfluidic models,syngeneic models,xenograft models,and genetically engineered models.We have also provided a concise summary of the merits and limitations inherent to each model while identifying the optimal contexts for their effective utilization.This review serves as a comprehensive resource,aiding researchers in making well-informed decisions regarding model selection that align with specific research questions.

Light at night and lung cancer risk:A worldwide interdisciplinary and time-series study

Background:Light at night(LAN)has become a concern in interdisciplinary research in recent years.This global interdisciplinary study aimed to explore the exposure-lag-response association between LAN exposure and lung cancer incidence.Methods:LAN data were obtained from the Defense Meteorological Satellite Program’s Operational Linescan System.Data of lung cancer incidence,socio-demographic index,and smoking prevalence of populations in 201 countries/territories from 1992 to 2018 were collected from the Global Burden of Disease Study.Spearman correlation tests and population-weighted linear regression analysis were used to evaluate the correlation between LAN exposure and lung cancer incidence.A distributed lag nonlinear model(DLNM)was used to assess the exposure-lag effects of LAN exposure on lung cancer incidence.Results:The Spearman correlation coefficients were 0.286-0.355 and the population-weighted linear regression correlation coefficients were 0.361-0.527.After adjustment for socio-demographic index and smoking preva-lence,the Spearman correlation coefficients were 0.264-0.357 and the population-weighted linear regression correlation coefficients were 0.346-0.497.In the DLNM,the maximum relative risk was 1.04(1.02-1.06)at LAN exposure of 8.6 with a 2.6-year lag time.After adjustment for socio-demographic index and smoking prevalence,the maximum relative risk was 1.05(1.02-1.07)at LAN exposure of 8.6 with a 2.4-year lag time.Conclusion:High LAN exposure was associated with increased lung cancer incidence,and this effect had a specific lag period.Compared with traditional individual-level studies,this group-level study provides a novel paradigm of effective,efficient,and scalable screening for risk factors.