Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model.

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems.

Rapid Screening of Consumer Products by GCxGC-HRT and Machine Learning Assisted Data Processing.

This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved.

Newborn Screening for Duchenne Muscular Dystrophy: First Year Results of a Population-Based Pilot.

Advancements in therapies for Duchenne muscular dystrophy (DMD) have made diagnosis within the newborn period a high priority. We undertook a consortia approach to advance DMD newborn screening in the United States. This manuscript describes the formation of the Duchenne Newborn Screening Consortium, the development of the pilot protocols, data collection tools including parent surveys, and findings from the first year of a two-year pilot. The DMD pilot design is population-based recruitment of infants born in New York State. Data tools were developed to document the analytical and clinical validity of DMD NBS, capture parental attitudes, and collect longitudinal health information for diagnosed newborns. Data visualizations were updated monthly to inform the consortium on enrollment. After 12 months, 15,754 newborns were screened for DMD by the New York State Newborn Screening (NYS NBS) Program. One hundred and forty screened infants had borderline screening results, and sixteen infants were referred for molecular testing. Three male infants were diagnosed with dystrophinopathy. Data from the first year of a two-year NBS pilot for DMD demonstrate the feasibility of NBS for DMD. The consortia approach was found to be a useful model, and the Newborn Screening Translational Research Network's data tools played a key role in describing the NBS pilot findings and engaging stakeholders.

Using Long-Term Follow-Up Data to Classify Genetic Variants in Newborn Screened Conditions.

With the rapid increase in publicly available sequencing data, healthcare professionals are tasked with understanding how genetic variation informs diagnosis and affects patient health outcomes. Understanding the impact of a genetic variant in disease could be used to predict susceptibility/protection and to help build a personalized medicine profile. In the United States, over 3.8 million newborns are screened for several rare genetic diseases each year, and the follow-up testing of screen-positive newborns often involves sequencing and the identification of variants. This presents the opportunity to use longitudinal health information from these newborns to inform the impact of variants identified in the course of diagnosis. To test this, we performed secondary analysis of a 10-year natural history study of individuals diagnosed with metabolic disorders included in newborn screening (NBS). We found 564 genetic variants with accompanying phenotypic data and identified that 161 of the 564 variants (29%) were not included in ClinVar. We were able to classify 139 of the 161 variants (86%) as pathogenic or likely pathogenic. This work demonstrates that secondary analysis of longitudinal data collected as part of NBS finds unreported genetic variants and the accompanying clinical information can inform the relationship between genotype and phenotype.

The Longitudinal Pediatric Data Resource: Facilitating Longitudinal Collection of Health Information to Inform Clinical Care and Guide Newborn Screening Efforts.

The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease and to assess the impact of early identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2200 with a range from 69 to 7944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of discoveries into clinical practice.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

The Human Phenotype Ontology in 2021.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

Lysyl oxidase like-2 contributes to renal fibrosis in Col4α3/Alport mice.

Lysyl oxidase like-2 (LOXL2) is an amine oxidase with both intracellular and extracellular functions. Extracellularly, LOXL2 promotes collagen and elastin crosslinking, whereas intracellularly, LOXL2 has been reported to modify histone H3, stabilize SNAIL, and reduce cell polarity. Although LOXL2 promotes liver and lung fibrosis, little is known regarding its role in renal fibrosis. Here we determine whether LOXL2 influences kidney disease in COL4A3 (-/-) Alport mice. These mice were treated with a small molecule inhibitor selective for LOXL2 or with vehicle and assessed for glomerular sclerosis and fibrosis, albuminuria, blood urea nitrogen, lifespan, pro-fibrotic gene expression and ultrastructure of the glomerular basement membrane. Laminin α2 deposition in the glomerular basement membrane and mesangial filopodial invasion of the glomerular capillaries were also assessed. LOXL2 inhibition significantly reduced interstitial fibrosis and mRNA expression of MMP-2, MMP-9, TGF-ß1, and TNF-α. LOXL2 inhibitor treatment also reduced glomerulosclerosis, expression of MMP-10, MMP-12, and MCP-1 mRNA in glomeruli, and decreased albuminuria and blood urea nitrogen. Mesangial filopodial invasion of the capillary tufts was blunted, as was laminin α2 deposition in the glomerular basement membrane, and glomerular basement membrane ultrastructure was normalized. There was no effect on lifespan. Thus, LOXL2 plays an important role in promoting both glomerular and interstitial pathogenesis associated with Alport syndrome in mice. Other etiologies of chronic kidney disease are implicated with our observations.

Effects of complex hydrodynamic processes on the horizontal and vertical distribution of Tc-99 in the Irish Sea.

The increased discharge of Tc-99 from the Sellafield plant following the commissioning of the Enhance Actinide Removal Plant in 1994 was reflected in higher Tc-99 activity concentrations over much of the Irish Sea. The presence of this radionuclide in the marine environment is of concern not only because of its long half life but also high bio-concentration factor in commercially valuable species, such Norway lobster (Nephrops norvegicus) and common lobster (Homarus gammarus). Accurate predictions of the transport, and spatial and temporal distributions of Tc-99 in the Irish Sea have important environmental and commercial implications. In this study, transport of the Tc-99 material was simulated in order to develop an increased understanding of long-term horizontal and vertical distributions. In particular, impact of seasonal hydrodynamic features such as the summer stratification on the surface-to-bottom Tc-99 ratio was of interest. Also, material retention mechanisms within the western Irish Sea were explored and flushing rates under various release conditions and meteorological forcing were estimated. The results show that highest vertical gradients are observed between June and July in the deepest regions of the North Channel and the western Irish Sea where radionuclide-rich saline-poor water overlays radionuclide-poor saline-rich Atlantic water masses. Strong correlation between top-to-bottom ratio of Tc-99 and strength of stratification was found. Flushing studies demonstrate that as the stratification intensifies, residence times within the western Irish Sea increase. In stratified waters of the gyre Tc-99 material is flushed out from the upper layer much quicker than from the bottom zone. The research also shows that in the gyre the biologically active upper layers above the thermocline are likely to contain higher concentrations than the near-bed region. Long-term horizontal and vertical distributions as determined in this study provide a basis for assessment of a potential biota exposure to Tc-99.

Assessment of Tc-99 monitoring within the western Irish Sea using a numerical model.

Water circulation patterns and associated material transport within a highly dynamic system such as the Irish Sea are complex phenomena. Although Tc-99 monitoring programme undertaken by the Radiological Protection Institute of Ireland provides a good insight to the material distribution on the east coast of Ireland, transport patterns within the Irish Sea have not been fully explored. In this study a validated transport model was used to hindcast transport of Tc-99 discharged from the Sellafield plant to determine extents of Tc-99 migration within the Irish Sea and reassess transit times to east coast of Ireland. Transit times are also estimated within a context of changes in meteorological conditions and fluctuations in discharges. Additionally, seasonal and inter-annual circulation patterns were examined. Relationships between discharge times and timing of far field concentrations are highly variable and are dependent on sea dynamics controlling the accumulation and removal of Tc-99 mass. Transport towards the Irish east coast, and consequently transit times, vary intra- and inter-annually, and depend on the prevailing hydrodynamic conditions resulting from meteorological conditions. The transit times from Sellafield to Balbriggan fall within the wide range of 30-240 days; with summer releases resulting in the shortest transit times. The model also indicated a strong relationship between summer concentration peaks on the east coast of Ireland and the strength of the Western Irish Gyre. Sudden increases of Tc-99 concentrations at Balbriggan coincide with peak of sea surface temperatures when the gyre is strongest and when advection is fastest. The adequacy of the current radionuclide monitoring programme within the western Irish Sea is evaluated, and recommendations are made for the development of a more optimised monitoring programme.

Investigations into the transport and pathways of scallop larvae--the use of numerical models for managing fish stocks.

The scallop fisheries off the southeast coast of Ireland have historically been considered a valuable resource for coastal communities, and hence their management is important. The scallop fishing grounds consist of a number of scallop beds dispersed throughout the St George's Channel and the southern Irish Sea. The boundaries between stocks and the interconnection in populations of adult scallops, through larval transport, is generally unknown. Until the time of this research, the stocks of scallop in this region had not been assessed. A research project was undertaken to develop novel, spatially explicit and multi-disciplinary approaches to the assessment of the scallop fisheries in the region. The project supported research in three related areas under the broad objective of developing stock assessment protocols and methods in order to promote sustainable management of these fisheries. One of these areas was the development and application of numerical models. The objectives of this modelling research were: (a) to reconstruct the physical environment of the study area; (b) investigate how this environment affects the demographics and nature of scallop; and (c) determine migration-transport pathways of the scallop larvae. From these investigations, light is shed on how the spatial variability in certain parameters of the natural environment determine habitats and the scallop populations. Also, the investigations now enable determinations to be made on the interconnectivities of 'disparate' scallop beds and where larvae are, in general, likely to be found. Thus, through the use of complex computer models, important clues are deduced that enable us to now understand key behavioural components of scallop larvae and their transport pathways. The analysis of these clues is assisting the development of protocols for managing the fish stock in a sustainable manner.

Modelling the fate and transport of nickel in the mersey estuary.

Modelling heavy metals in estuarine environments is extremely complex for various reasons; one of the primary complicating factors is that metals exist in two phases, dissolved and particulate bound. Dynamic changes in water chemistry, and in particular salinity, affect the partitioning of metals between the two phases and hence make it difficult to determine the relative fractions of each phase. A relatively simple approach was developed to relate variations in partition coefficient for Ni to salinity fluctuations in the Mersey Estuary. The functional relationship developed between partition coefficient and salinity departs from the traditional exponential type curve, providing a more realistic relationship.A numerical model was then developed for predicting the transport and distribution of Ni about the Mersey Estuary. The model couples transport of metals throughout the water along with incorporating the chemical processes controlling how nickel is fractioned between dissolved and particulate phases through the newly developed partition coefficient relationship. Model predictions of dissolved Ni along the longitudinal axis of the estuary were compared with measurements of Ni for two events; very good correlation was obtained between the model results and the data.