Influence of ultraviolet A radiation on osmolytes transport in human retinal pigment epithelial cells

AIM: To demonstrate that ultraviolet A(UVA)induces osmolytes accumulation in retinal pigment epithelial(RPE)cells.<p>METHODS: Under different experimental conditions such as UVA exposure, hyperosmotic stress condition and hypoosmotic stress condition, RPE cells were cultured for different time periods. The betaine /γ-amino- n-butyric acid(GABA)transporter, the sodium-dependent myoinositol transporter and the taurine transporter(TAUT)mRNA were measured by quantitative PCR. The radioactive labeled osmolytes were measured to evaluate the level of osmolytes transportation. <p>RESULTS: This study demonstrated that RPE expressed mRNA specific for the betaine/GABA transporter, for the sodium-dependent myoinositol transporter and for the TAUT. In comparison to norm osmotic(300mosmol/L)controls, a 3-5-fold induction of mRNA expression for the betaine/GABA transporter, the sodium-dependent myoinositol transporter and the TAUT was observed within 6-24h after hyperosmotic exposure(400mosmol/L). Expression of osmolyte transporters was associated with an increased uptake of radioactive labeled osmolytes. Conversely, hypoosmotic(200mosmol/L)stimulation induced significant efflux of these osmolytes. UVA significantly stimulated osmolyte uptake. Increased osmolyte uptake was associated with upregulation of mRNA steady-state levels for osmolyte transporters in irradiated cells.<p>CONCLUSION: UVA induces osmolyte uptake in RPE. It is similar reaction to hyperosmotic stress. This suggests that osmolyte uptake response by UVA may be important to maintain homeostasis

Classification of integers based on residue classes via modern deep learning algorithms

Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches on classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We also evaluated Automated Machine Learning (AutoML) platforms from Amazon, Google and Microsoft, and found that they failed on this task without appropriately engineered features. Furthermore, we introduced a method that utilizes linear regression on Fourier series basis vectors, and demonstrated its effectiveness. Finally, we evaluated Large Language Models (LLMs) such as GPT-4, GPT-J, LLaMA and Falcon, and demonstrated their failures. In conclusion, feature engineering remains an important task to improve performance and increase interpretability of machine-learning models, even in the era of AutoML and LLMs.Comment: Accepted at Pattern