Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review.

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

Evidence for extensive parallelism but divergent genomic architecture of adaptation along altitudinal and latitudinal gradients in Populus trichocarpa.

Adaptation to climate across latitude and altitude reflects shared climatic constraints, which may lead to parallel adaptation. However, theory predicts that higher gene flow should favor more concentrated genomic architectures, which would lead to fewer locally maladapted recombinants. We used exome capture to resequence the gene space along a latitudinal and two altitudinal transects in the model tree Populus trichocapra. Adaptive trait phenotyping was coupled with FST outlier tests and sliding window analysis to assess the degree of parallel adaptation as well as the genomic distribution of outlier loci. Up to 51% of outlier loci overlapped between transect pairs and up to 15% of these loci overlapped among all three transects. Genomic clustering of adaptive loci was more pronounced for altitudinal than latitudinal transects. In both altitudinal transects, there was a larger number of these 'islands of divergence', which were on average longer and included several of exceptional physical length. Our results suggest that recapitulation of genetic clines over latitude and altitude involves extensive parallelism, but that steep altitudinal clines generate islands of divergence. This suggests that physical proximity of genes in coadapted complexes may buffer against the movement of maladapted alleles from geographically proximal but climatically distinct populations.

Partitioning of multivariate phenotypes using regression trees reveals complex patterns of adaptation to climate across the range of black cottonwood (Populus trichocarpa).

Local adaptation to climate in temperate forest trees involves the integration of multiple physiological, morphological, and phenological traits. Latitudinal clines are frequently observed for these traits, but environmental constraints also track longitude and altitude. We combined extensive phenotyping of 12 candidate adaptive traits, multivariate regression trees, quantitative genetics, and a genome-wide panel of SNP markers to better understand the interplay among geography, climate, and adaptation to abiotic factors in Populus trichocarpa. Heritabilities were low to moderate (0.13-0.32) and population differentiation for many traits exceeded the 99th percentile of the genome-wide distribution of FST, suggesting local adaptation. When climate variables were taken as predictors and the 12 traits as response variables in a multivariate regression tree analysis, evapotranspiration (Eref) explained the most variation, with subsequent splits related to mean temperature of the warmest month, frost-free period (FFP), and mean annual precipitation (MAP). These grouping matched relatively well the splits using geographic variables as predictors: the northernmost groups (short FFP and low Eref) had the lowest growth, and lowest cold injury index; the southern British Columbia group (low Eref and intermediate temperatures) had average growth and cold injury index; the group from the coast of California and Oregon (high Eref and FFP) had the highest growth performance and the highest cold injury index; and the southernmost, high-altitude group (with high Eref and low FFP) performed poorly, had high cold injury index, and lower water use efficiency. Taken together, these results suggest variation in both temperature and water availability across the range shape multivariate adaptive traits in poplar.