Influence of filter age on Fe, Mn and NH4+ removal in dual media rapid sand filters used for drinking water production

Rapid sand filtration is a common method for removal of iron (Fe), manganese (Mn) and ammonium (NH4+) from anoxic groundwaters used for drinking water production. In this study, we combine geochemical and microbiological data to assess how filter age influences Fe, Mn and NH4+ removal in dual media filters, consisting of anthracite overlying quartz sand, that have been in operation for between ∼2 months and ∼11 years. We show that the depth where dissolved Fe and Mn removal occurs is reflected in the filter medium coatings, with ferrihydrite forming in the anthracite in the top of the filters ( 1 m). Removal of NH4+ occurs through nitrification in both the anthracite and sand and is the key driver of oxygen loss. Removal of Fe is independent of filter age and is always efficient (> 97% removal). In contrast, for Mn, the removal efficiency varies with filter age, ranging from 9 to 28% at ∼2–3 months after filter replacement to 100% after 8 months. After 11 years, removal reduces to 60–80%. The lack of Mn removal in the youngest filters (at 2–3 months) is likely the result of a relatively low abundance of mineral coatings that adsorb Mn2+ and provide surfaces for the establishment of a microbial community. 16S rRNA gene amplicon sequencing shows that Gallionella, which are known Fe2+ oxidizers, are present after 2 months, yet Fe2+ removal is mostly chemical. Efficient NH4+ removal (> 90%) establishes within 3 months of operation but leakage occurs upon high NH4+loading (> 160 µM). Two-step nitrification by Nitrosomonas and Candidatus Nitrotoga is likely the most important NH4+ removal mechanism in younger filters during ripening (2 months), after which complete ammonia oxidation by Nitrospira and canonical two-step nitrification occur simultaneously in older filters. Our results highlight the strong effect of filter age on especially Mn2+but also NH4+ removal. We show that ageing of filter medium leads to the development of thick coatings, which we hypothesize leads to preferential flow, and breakthrough of Mn2+. Use of age-specific flow rates may increase the contact time with the filter medium in older filters and improve Mn2+ and NH4+ removal

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility

A review of nitrous oxide mitigation by farm nitrogen management in temperate grassland-based agriculture

peer-reviewedNitrous oxide (N2O) emission from grassland-based agriculture is an important source of atmospheric N2O. It is hence crucial to explore various solutions including farm nitrogen (N) management to mitigate N2O emissions without sacrificing farm profitability and food supply. This paper reviews major N management practices to lower N2O emission from grassland-based agriculture. Restricted grazing by reducing grazing time is an effective way to decrease N2O emissions from excreta patches. Balancing the protein-to-energy ratios in the diets of ruminants can also decrease N2O emissions from excreta patches. Among the managements of synthetic fertilizer N application, only adjusting fertilizer N rate and slow-released fertilizers are proven to be effective in lowering N2O emissions. Use of bedding materials may increase N2O emissions from animal houses. Manure storage as slurry, manipulating slurry pH to values lower than 6 and storage as solid manure under anaerobic conditions help to reduce N2O emissions during manure storage stage. For manure land application, N2O emissions can be mitigated by reducing manure N inputs to levels that satisfy grass needs. Use of nitrification inhibitors can substantially lower N2O emissions associated with applications of fertilizers and manures and from urine patches. N2O emissions from legume based grasslands are generally lower than fertilizer-based systems. In conclusion, effective measures should be taken at each step during N flow or combined options should be used in order to mitigate N2O emission at the farm level.Department of Agriculture, Fisheries and Food, Ireland, Research Stimulus Fund (RSF 07 516, RSF 07 511