Soil pollution caused by heavy metal(oid)s has generated great concern worldwide due to their toxicity, persistence, and bio-accumulation properties. To assess the baseline data, the heavy metal(oid)s, including manganese (Mn), iron (Fe), Cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), mercury (Hg), chromium (Cr), and cadmium (Cd), were evaluated in surface soil samples collected from the farmlands of Grand Forks County, North Dakota. Samples were digested via acid mixture and analyzed via inductively coupled plasma mass spectrometry (ICP MS) analysis to assess the levels, ecological risks, and possible sources. The heavy metal(oid) median levels exhibited the following decreasing trend: Fe > Mn > Zn > Ni > Cr > Cu > Pb > Co > As > Cd > Hg. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) suggested the main lithogenic source for the studied metal(oid)s. Metal(oid) levels in the current investigation, except Mn, are lower than most of the guideline values set by international agencies. The contamination factor (C_f), geo accumulation index (I_geo) and enrichment factor (EF) showed considerable contamination, moderate contamination, and significant enrichment, respectively, for As and Cd on median value basis. Ecological risk factor (E_r) results exhibited low ecological risk for all studied metal(oid)s except Cd, which showed considerable ecological risk. The potential ecological risk index (PERI) levels indicated low ecological risk to considerable risk. Overall, the results indicate the accumulation of As and Cd in the study area. The high nutrients of the soils potentially affect their accumulation in crops and impact on consumers’ health. This drives the impetus for continued environmental monitoring programs.

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies’ increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

Heavy metal (HM) pollution of soil is an increasingly serious problem worldwide. The current study assessed the metal levels and ecological and human health risk associated with HMs in Grand Forks urban soils. A total 40 composite surface soil samples were investigated for Mn, Fe, Co, Ni, Cu, Zn, As, Pb, Hg, Cr, Cd and Tl using microwave-assisted HNO₃-HCl acid digestion and inductively coupled plasma mass spectrometry (ICP-MS) analysis. The enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), ecological risk and potential ecological risk index were used for ecological risk assessment. The park soils revealed the following decreasing trend for metal levels: Fe > Mn > Zn > Cr > Ni > Cu > Pb > As > Co > Cd > Tl > Hg. Based on mean levels, all the studied HMs except As and Cr were lower than guideline limits set by international agencies. Principal component analysis (PCA) indicated that Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Pb, Cr and Tl may originate from natural sources, while Hg, Pb, As and Cd may come from anthropogenic/mixed sources. The Igeo results showed that the soil was moderately polluted by As and Cd and, based on EF results, As and Cd exhibited significant enrichment. The contamination factor analysis revealed that Zn and Pb showed moderate contamination, Hg exhibited low to moderate contamination and As and Cd showed high contamination in the soil. Comparatively higher risk was noted for children over adults and, overall, As was the major contributor (>50%), followed by Cr (>13%), in the non-carcinogenic risk assessment. Carcinogenic risk assessment revealed that As and Cr pose significant risks to the populations associated with this urban soil. Lastly, this study showed that the soil was moderately contaminated by As, Cd, Pb and Hg and should be regularly monitored for metal contamination.

Heavy metal contamination in food has become a significant global food safety concern. This study assessed the concentrations of As, Ca, Cd, Co, Cr, Cu, Fe, Hg, Mn, K, Mg, Na, Ni, Se, Pb, and Zn in 13 locally grown vegetables using microwave-assisted acid digestion and ICP-MS. The potential human health risks associated with their consumption were also evaluated. Vegetable samples were collected from the local farmer’s market in Grand Forks, North Dakota. The mean levels (μg/g) of Na, Mg, K, Ca, Fe, Se, Mn, Cu, Zn, Co, Hg, Cr, Ni, As, Cd, and Pb were 1001, 2935, 30474, 686.0, 52.90, 0.171, 37.63, 4.936, 21.33, 0.069, 0.0030, 0.049, 0.736, 0.083, 0.298, and 0.019, respectively, having the following decreasing trend: K > Mg > Na > Ca > Fe > Mn > Zn > Cu > Ni > Cd > Se > As > Co > Cr > Pb > Hg. The highest total metals level was found in spinach, with the following decreasing order: spinach > tomato > sugar beet > white eggplant > cucumber ~ kale > green chili > green bean > dill ~ potato > capsicum > onion > corn. Spinach exhibited the highest concentrations of Cd, Cr, Pb, and Hg, which suggests a higher risk of metal exposure from its consumption. Toxic metals except Cd were found to be lower than the maximum allowable concentrations set by international agencies among the analyzed vegetables, while Cd levels were higher than maximum allowable levels in most of the vegetables. Health risks associated with metal intake by vegetable consumption were evaluated in terms of estimated daily intake (EDI), non-carcinogenic risks were evaluated by the target hazard quotient (THQ) and Hazard Index (HI), and carcinogenic risks were evaluated by target cancer risk (TCR). The EDI values of all the metals were found to be below the maximum tolerable daily intake (MTDI). The highest EDI value for Mn, Zn, Hg, Cr, Cd, and Pb was noted in spinach. THQ values for Cd, Co, and As were higher than 1 in most of the vegetable species analyzed, indicating non-carcinogenic health effects to consumers. HI results also posed a non-carcinogenic health risk associated with the intake of these vegetables. Mean TCR values of Cr, Ni, As, and Cd indicated carcinogenic risk for consumers. This study showed that there are potential health risks with consumption of these vegetables. Lastly, regular monitoring of metal levels in vegetables is suggested/recommended to minimize health risks and support pollution control efforts.

: Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights:

The human kidney is known to possess renal progenitor cells (RPCs) that can assist in the repair of acute tubular injury. The RPCs are sparsely located as single cells throughout the kidney. We recently generated an immortalized human renal progenitor cell line (HRTPT) that co-expresses PROM1/CD24 and expresses features expected on RPCs. This included the ability to form nephrospheres, differentiate on the surface of Matrigel, and undergo adipogenic, neurogenic, and osteogenic differentiation. These cells were used in the present study to determine how the cells would respond when exposed to nephrotoxin. Inorganic arsenite (iAs) was chosen as the nephrotoxin since the kidney is susceptible to this toxin and there is evidence of its involvement in renal disease. Gene expression profiles when the cells were exposed to iAs for 3, 8, and 10 passages (subcultured at 1:3 ratio) identified a shift from the control unexposed cells. The cells exposed to iAs for eight passages were then referred with growth media containing no iAs and within two passages the cells returned to an epithelial morphology with strong agreement in differential gene expression between control and cells recovered from iAs exposure. Results show within three serial passages of the cells exposed to iAs there was a shift in morphology from an epithelial to a mesenchymal phenotype. EMT was suggested based on an increase in known mesenchymal markers. We found RPCs can undergo EMT when exposed to a nephrotoxin and undergo MET when the agent is removed from the growth media.