Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Nanosecond time scale motions in proteins revealed by high-resolution NMR relaxometry.

Understanding the molecular determinants underlying protein function requires the characterization of both structure and dynamics at atomic resolution. Nuclear relaxation rates allow a precise characterization of protein dynamics at the Larmor frequencies of spins. This usually limits the sampling of motions to a narrow range of frequencies corresponding to high magnetic fields. At lower fields one cannot achieve sufficient sensitivity and resolution in NMR. Here, we use a fast shuttle device where the polarization builds up and the signals are detected at high field, while longitudinal relaxation takes place at low fields 0.5 < B0 < 14.1 T. The sample is propelled over a distance up to 50 cm by a blowgun-like system in about 50 ms. The analysis of nitrogen-15 relaxation in the protein ubiquitin over such a wide range of magnetic fields offers unprecedented insights into molecular dynamics. Some key regions of the protein feature structural fluctuations on nanosecond time scales, which have so far been overlooked in high-field relaxation studies. Nanosecond motions in proteins may have been underestimated by traditional high-field approaches, and slower supra-τ(c) motions that have no effect on relaxation may have been overestimated. High-resolution relaxometry thus opens the way to a quantitative characterization of nanosecond motions in proteins.

Synthesis of Carbon Nanotubes (CNTs) from Poultry Litter for Removal of Chromium (Cr (VI)) from Wastewater.

Pakistan, an agricultural country, raises 146.5 million commercial and domestic poultry birds, which generate around 544,831 tons of waste per year. This waste finds its final disposal in agricultural land as soil fertilizer or disposal site amendment. The usage of poultry litter for this purpose is uncontrolled, which results in environmental degradation such as emission of greenhouse gases, e.g., methane. However, alternative options such as thermochemical conversion of poultry litter can offer better solutions where this waste can be used as a low-cost carbon source for the synthesis of Multiwalled Carbon Nanotubes (MWCNTs). In this study, efforts were made to utilize this cheap and plentiful carbon source for the synthesis of CNTs in the presence of Ni/Mo/MgO as a catalyst, through pyrolysis. For a better yield of carbon product, the optimum ratio for the catalysts (Ni/Mo/MgO) was found to be 4:0.2:1. Furthermore, the process parameters were also optimized for better carbon yield. A good yield of CNTs resulted from a pyrolysis time of 12 min, a temperature of 825 °C, and a catalyst weight of 100 mg. The structure and morphology of the produced nanotubes were confirmed through X-ray Diffractometer (X-RD) and Scanning Electron Microscopy (SEM). The environmental application of the nanotubes was tested in a synthetic chromium solution in the lab using a batch experiment. Different experimental conditions (pH, adsorbent dosage, and contact time) were optimized to improve the adsorption of Cr (VI) by carbon nanotubes and a UV-Visible spectrophotometer was used at 540 nm to measure the absorbance of Cr (VI). The results showed that up to 81.83% of Cr (VI) removal was achieved by using 8 mg of CNTs at pH 3 with 400 rpm at 180 min of contact time. Thus, it was concluded that poultry litter can be a useful source for the synthesis of MWCNTs and thereby removal of Cr (VI) from industrial tanneries' wastewater.

Impact of land use/land cover changes on water quality and human health in district Peshawar Pakistan.

The quality and quantity of groundwater resources are affected by landuse/landcover (LULC) dynamics, particularly the increasing urbanization coupled with high household wastewater discharge and decreasing open lands. This study evaluates temporal changes of groundwater quality for 2012 and 2019, its relation to Landuse/landcover, and its impact on Peshawar's residents (study area), Pakistan. A total of 105 and 112 groundwater samples were collected from tube wells in 2012 and 2019. Samples were then analyzed for seven standard water quality parameters (i.e., pH, electric conductivity (EC), turbidity, chloride, calcium, magnesium, and nitrate). Patient data for waterborne diseases were also collected for the years 2012 and 2019 to relate the impact of groundwater quality on human health. Landsat satellite images were classified for the years 2012 and 2019 to observe landuse/landcover dynamics concerning groundwater quality. Results manifested a decrease in groundwater quality for the year 2019 compared to 2012 and were more highlighted in highly populated areas. The nitrate concentration level was found high in the vicinity of agricultural areas due to the excessive use of nitrogenous fertilizers and pesticides, and thus the methemoglobinemia patients ratio increased by 14% (48-62% for the year 2012 and 2019, respectively). Besides, Urinary Tract Infections, Peptic Ulcer, and Dental Caries diseases increased due to the high calcium and magnesium concentration. The overall results indicate that anthropogenic activities were the main driver of Spatio-temporal variability in groundwater quality of the study area. The study could help district health administration understand groundwater quality trends, make appropriate site-specific policies, and formulate future health regulations.