Genetic studies of extra-early provitamin-A maize inbred lines and their hybrids in multiple environments

Open Access Article; Published online: 25 Sept 2019Vitamin A deficiency, drought, low soil nitrogen (low N) and Striga hermonthica parasitism of maize (Zea mays L.) cause malnutrition and food insecurity in sub-Saharan Africa. The objectives of this study were to determine combining abilities of extra-early provitamin A (PVA) lines, classify them into heterotic groups (HGs), identify testers, and determine yield stability of hybrids under contrasting environments in two trials. In trial 1, 20 extra-early PVA lines were inter-mated in a diallel mating scheme to obtain 190 F1 hybrids. The 190 F1 hybrids plus six checks were tested under Striga infestation, drought, and stress-free environments in Nigeria from 2015 to 2017. In trial 2, 35 extra-early yellow hybrids were evaluated under low-N, Striga-infested and stress-free environments in 2018. Provitamin A concentrations of 23.98 and 22.56 μg g-1 were obtained for TZEEIOR 202 and TZEEIOR 205. TZEEIOR 197 × TZEEIOR 205 (20.1 μg g-1) and TZEEIOR 202 × TZEEIOR 205 (22.7 μg g-1) contained about double the PVA level of the commercial check, TZEEI 58 × TZEE-Y Pop STR C5 (11.4 μgg-1). Both general (GCA) and specific (SCA) combining ability variances were statistically significant for most agronomic traits, although GCA was much larger than SCA effects, indicating that additive genetic effects primarily controlled the inheritance of those traits. TZEEIOR 97 and TZEEIOR 197 were identified as inbred testers. TZEEIOR 197 × TZEEIOR205 (20.1 μg g-1) was identified as a single-cross tester as well as the most stable and highest-yielding hybrid across environments. TZEEIOR 202 and TZEEIOR 205 should be invaluable resources for breeding for high PVA. PVA level was independent of hybrid yield potential, indicating that selection of superior hybrids with elevated PVA levels should be feasible

Semi-automated validation and quantification of CTLA-4 in 90 different Tumor entities using multiple antibodies and artificial intelligence

Abstract Introduction/Objective Introduction: CTLA-4 is an inhibitory immune checkpoint receptor and a negative regulator of anti-tumor T-cell function. This study aimed at a comparative analysis of CTLA-4+ entities. cells between different tumor Methods/Case Report Methods: To quantify CTLA-4+ cells, 4,582 tumor samples from 90 different tumor entities as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry in a tissue microarray format. Two different antibody clones (MSVA-152R and CAL49) were validated and quantified using a deep learning framework for automated exclusion of unspecific immunostaining. Results (if a Case Study enter NA) Results: Comparing both CTLA-4 antibodies revealed a clone dependent unspecific staining pattern in adrenal cortical adenoma (63%) for MSVA-152R and in pheochromocytoma (67%) as well as hepatocellular carcinoma (36%) for CAL49. After automated exclusion of non-specific staining reaction (3.6%), a strong correlation was observed for the densities of CTLA-4+ lymphocytes obtained by both antibodies (r=0.87; p&amp;lt;0.0001). The mean density of CTLA-4+cells was 674±1482 cells/ mm2 and ranged from 71±175 cells/mm2 in leiomyoma to 5916±3826 cells/mm2 in Hodgkin’s lymphoma. Within epithelial tumors, the density of CTLA-4+ lymphocytes were higher in squamous cell (421±467 cells/ mm2) and urothelial carcinomas (419±347 cells/ mm2) than in adenocarcinomas (269±375 cells/ mm2) and renal cell neoplasms (256±269 cells/ mm2). A high CTLA-4+ cell density was linked to low pT category (p&amp;lt;0.0001), absent lymph node metastases (p=0.0354), and PD-L1 expression in tumor cells or inflammatory cells (p&amp;lt;0.0001 each). A high CTLA-4/CD3-ratio was linked to absent lymph node metastases (p=0.0295) and to PD-L1 positivity on immune cells (p&amp;lt;0.0026). Conclusion Marked differences exist in the number of CTLA-4+ lymphocytes between tumors. Analyzing two independent antibodies by a deep learning framework can facilitate automated quantification of immunohistochemically analyzed target proteins such as CTLA-4. </jats:sec