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Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science.

Domain adaptation is an attractive approach given the availability of a large amount of labeled data with similar properties but different domains. It is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods for effectively solving real-world problems. The major assumption is that when base domain adaptation models are combined, we can obtain a more accurate and robust model by exploiting the ability of each of the base models. We extend Maximum Mean Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to compute the adaptation loss in three base models. Also, we utilize a two-fully connected layer network as a meta-model to stack the output predictions of these three well-performing domain adaptation models to obtain high accuracy in ophthalmic image classification tasks. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.

Due to the high solubility of nitrate in water, it is the most widespread contaminant in drinking water sources. In this study, activated carbon (AC) and a composite of activated carbon and Fe2O3 nanoparticles (Fe–AC) were used for nitrate removal from water. AC and Fe–AC adsorbents were characterized using BET, SEM, FTIR and XRF analysis. The main operating parameters such as initial concentration (C0), adsorbent dosage and pH have been optimized for maximum nitrate removal. Experimental design was carried out using Central Composite Design (CCD) with response surface methodology (RSM). Based on RSM analysis, the nitrate removal models proved to be highly significant with very low probability values (