Generation of induced pluripotent stem cell lines from patients with LQT1 caused by heterozygous mutations in the KCNQ1 gene.

Long QT Syndrome (LQTS) is a genetic heart disorder that can induce cardiac arrhythmias. The most prevalent subtype, LQT1, stems from rare variants in the KCNQ1 gene. Utilizing induced pluripotent stem cells (iPSCs) enables detailed cellular studies and personalized medicine approaches for this life-threatening condition. We generated two LQT1 iPSC lines with single nucleotide nonsense mutations, c.1031 C > T and c.1121 T > A in KCNQ1. Both lines exhibited typical iPSC morphology, expressed high levels of pluripotent markers, maintained normal karyotype, and possessed the capability to differentiate into three germ layers. These cell lines serve as important tools for investigating the biological mechanisms underlying LQT1 due to mutations in the KCNQ1 gene.

Generation of induced pluripotent stem cell lines from South Asian ethnicity.

South Asians, which represent around 25% of the world's population, have a disproportionately high risk of cardiometabolic disease, two-fold higher risk of myocardial infarction, and 4- to 6-fold higher risk for diabetes compared to Caucasians. We generated two induced pluripotent stem cell (iPSC) lines from healthy South Asian donors and validated the pluripotency and ability of these cell lines to differentiate into three germ layers. These iPSC lines can be applied to generate many cardiovascular cell types such as cardiomyocytes, endothelial cells, and mural cells to investigate different cardiovascular disease mechanisms triggered by environmental risk factors or drugs in vitro.

Leaf spectroscopy of resistance to Ceratocystis wilt of 'Ohi'a.

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of 'ohi'a, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or 'Ohi'a, Hawaii's most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C. lukuohia is detectable in leaf-level reflectance spectra (400-2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2-3-year-old M. polymorpha clones with C. lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of 'ohi'a resistance screenings.

Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture.

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.