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

Magnetic reversal and pinning in a perpendicular zero-moment half-metal

Compensated ferrimagnets are promising materials for fast spintronic applications based on domain-wall motion as they combine the favorable properties of ferromagnets and antiferromagnets. They inherit from antiferromagnets immunity to external fields, fast spin dynamics, and rapid domain-wall motion. From ferromagnets they inherit straightforward ways to read out the magnetic state, especially in compensated half metals, where electrons flow in only one spin channel. Here, we investigate domain structure in compensated half-metallic Mn2Ru0.5Ga films and assess their potential in domain-wall motion-based spin-electronic devices. Our focus is on understanding and reducing domain-wall pinning in unpatterned epitaxial thin films. Two modes of magnetic reversal, driven by nucleation or domain-wall motion, are identified for different thin film deposition temperatures (Tdep). The magnetic aftereffect is analyzed to extract activation volumes (V∗), activation energies (EA), and their variation (ΔEA). The latter is decisive for the magnetic reversal regime, where domain-wall motion dominated reversal (weak pinning) is found for ΔEA0.5eV. A minimum ΔEA=28meV is found for Tdep=290∘C. Prominent pinning sites are visualized by analyzing virgin domain patterns after thermal demagnetization. In the sample investigated they have spacings of order 300 nm, which gives an upper limit of the track width of spin-torque domain-wall motion-based devices.This project has received funding from Science Foundation Ireland through Contracts No. 16/IA/4534 ZEMS and No. 12/RC/2278 AMBER and from the European Union’s FET-Open research programme under Grant Agreement No. 737038. N.T. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie EDGE Grant agreement No. 713567. We also gratefully acknowledge funding from Northern Ireland’s Department for Economy through USIreland Grant No. USI 108

The Smc5–Smc6 Complex Is Required to Remove Chromosome Junctions in Meiosis

Meiosis, a specialized cell division with a single cycle of DNA replication round and two consecutive rounds of nuclear segregation, allows for the exchange of genetic material between parental chromosomes and the formation of haploid gametes. The structural maintenance of chromosome (SMC) proteins aid manipulation of chromosome structures inside cells. Eukaryotic SMC complexes include cohesin, condensin and the Smc5–Smc6 complex. Meiotic roles have been discovered for cohesin and condensin. However, although Smc5–Smc6 is known to be required for successful meiotic divisions, the meiotic functions of the complex are not well understood. Here we show that the Smc5–Smc6 complex localizes to specific chromosome regions during meiotic prophase I. We report that meiotic cells lacking Smc5–Smc6 undergo catastrophic meiotic divisions as a consequence of unresolved linkages between chromosomes. Surprisingly, meiotic segregation defects are not rescued by abrogation of Spo11-induced meiotic recombination, indicating that at least some chromosome linkages in smc5–smc6 mutants originate from other cellular processes. These results demonstrate that, as in mitosis, Smc5-Smc6 is required to ensure proper chromosome segregation during meiosis by preventing aberrant recombination intermediates between homologous chromosomes

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

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