A unified and multi-scale source:Pathway priority index for diffuse pollution management

Diffuse pollution is a global issue where management, particularly relating to phosphorus (P) transfers from agricultural land to water, needs to consider the magnitude of the source pressure and the connectivity of the hydrological pathway pressure. Combined, these pressures are considered as critical source areas (CSAs) and where mitigation resources should be focused as part of landscape targeting. However, data requirements and lack of a unified method have made this difficult to implement at national scales. To overcome this, a unique and transferrable national workflow is presented for this purpose at three scales to aid in prioritisation. First, macro- or basin-scale (100–600 km2) water quality data (soluble reactive P—SRP) were used as an initial indicator of pressures at a national river basin scale in Northern Ireland. Second, within these macro-scale catchments, meso‑scale catchments (10–100 km2) used for Water Framework Directive surveillance (n > 230) were prioritised using a validated relationship between long-term river SRP and soil test phosphorus (STP—Olsen P) as the source pressure in over 300,000 agricultural fields tested as part of a national monitoring programme. These meso‑scale catchments were also screened for persistent point source pressures using ammonium (NH4) concentration data. Within each meso‑scale catchment, micro-scale catchments (0.02 – 1.6 km2; 5th – 95th percentile) were identified (> 1.9 million) that combined summaries of STP and a runoff risk metric that was developed with a high-resolution (16 points m−2) LiDAR derived soil topographic index (STI) into an anonymised and dimensionless Source:Pressure Priority Index (SPPI). Exemplar outputs are shown in detail that weight the source and pathway pressures equally, and further emphasise source over pathway pressure, and vice versa, to ensure advisory and mitigation resources can be allocated effectively. The SPPI is a more robust diffuse pollution risk assessment and management tool as it recognises the importance of managing the magnitude of the source pressure, in combination with reducing pathway pressures, rather than focusing on the latter in isolation. This will ensure a faster route to diffuse pollution reduction and offer resilience as pathway mitigations become vulnerable to weather patterns and runoff responses in a changing climate

Quantifying MCPA load pathways at catchment scale using high temporal resolution data

Publication history: Accepted - 21 May 2022; Published online - 24 May 2022.Detection of the agricultural acid herbicide MCPA (2-methyl-4-chlorophenoxyacetic acid) in drinking water source catchments is of growing concern, with economic and environmental implications for water utilities and wider ecosystem services. MCPA is poorly adsorbed to soil and highly mobile in water, but hydrological pathway processes are relatively unknown at the catchment scale and limited by coarse resolution data. This understanding is required to target mitigation measures and to provide a framework to monitor their effectiveness. To address this knowledge gap, this study reports findings from river discharge and synchronous MCPA concentration datasets (continuous 7 hour and with additional hourly sampling during storm events) collected over a 7 month herbicide spraying season. The study was undertaken in a surface (source) water catchment (384 km2—of which 154 km2 is agricultural land use) in the cross-border area of Ireland. Combined into loads, and using two pathway separation techniques, the MCPA data were apportioned into event and baseload components and the former was further separated to quantify a quickflow (QF) and other event pathways. Based on the 7 hourly dataset, 85.2 kg (0.22 kg km 2 by catchment area, or 0.55 kg km 2 by agricultural area) of MCPA was exported from the catchment in 7 months. Of this load, 87.7 % was transported via event flow pathways with 72.0 % transported via surface dominated (QF) pathways. Approximately 12 % of the MCPA load was transported via deep baseflows, indicating a persistence in this delayed pathway, and this was the primary pathway condition monitored in a weekly regulatory sampling programme. However, overall, the data indicated a dominant acute, storm dependent process of incidental MCPA loss during the spraying season. Reducing use and/or implementing extensive surface pathway disconnection measures are the mitigation options with greatest potential, the success of which can only be assessed using high temporal resolution monitoring techniques.This work was carried out as part of Source to Tap (IVA5018), a project supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB)

Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017

© 2017 Association for Computing Machinery. Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, word affect features, which derive human affect ratings (e.g. arousal and valence) from transcripts, were investigated for predicting depression severity and liking, showing great promise. A simple system based on the word affect features achieved an RMSE of 6.02 on the test set, yielding a relative improvement of 13.6% over the baseline. For the emotion prediction sub-challenge, we investigated multimodal fusion, which incorporated a measure of uncertainty associated with each prediction within an Output-Associative fusion framework for arousal and valence prediction, whilst liking prediction systems mainly focused on text-based features. Our best emotion prediction systems provided significant relative improvements over the baseline on the test set of 39.5%, 17.6%, and 29.3% for arousal, valence, and liking. Of particular note is that consistent improvements were observed when incorporating prediction uncertainty across various system configurations for predicting arousal and valence, suggesting the importance of taking into consideration prediction uncertainty for fusion and more broadly the advantages of probabilistic predictions

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure

Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females’ central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements

Generic acquisition protocol for quantitative MRI of the spinal cord

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols. The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition