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Leaders in plain sight:what the experiences of early career social workers tell us about leadership in Scottish social work
No full textLittle is known about the application of leadership capabilities amongst early career social workers. This chapter draws from a five-year longitudinal study in Scotland exploring the work experiences and professional development of new social workers from the point of qualification. Findings indicate that dimensions of leadership are widely understood and enacted in everyday practice; however, professional capability and confidence in this area are restrained by the persistence of managerial organisational cultures and a diminished professional learning and development infrastructure. The Scottish government, employers, and the Scottish Social Services Council could do more to bridge the gap between well-intentioned policy objectives and the lived experience of early career social workers keen to build on their emerging leadership knowledge and skills.</p
Mutations within the predicted 1 fragment-binding region of FAM83G/SACK1G abolish its interaction with the Ser/Thr kinase CK1α
No full textSACK1G (aka FAM83G, PAWS1) plays a central role in activating canonical WNT signalling via interaction with the Ser/Thr kinase CK1α. This loss of CK1α binding and WNT signalling underlies the pathogenesis of Palmoplantar Keratoderma (PPK) caused by several reported mutations in the SACK1G gene. We modelled the scaffold anchor of CK1 (SACK1) domain of SACK1G and used fragment-bound structures of the SACK1B (FAM83B) dimer to guide our analysis. This allowed us to computafonally predict several key residues near the fragment binding site in SACK1G that may be important for its funcfon. We mutated these residues, introduced them into SACK1G-/- DLD-1 colorectal cancer cells and investigated their ability to bind endogenous CK1α. We uncovered two SACK1G mutations, namely Y204A and I206A, that abolish interaction with CK1α similarly to the PPK pathogenic mutant A34E. Consistent with this loss of SACK1G-CK1α interaction, the molecular glue degrader of CK1α, DEG-77, fails to co-degrade the Y204A and I206A mutants while it still co-degrades native SACK1G. Our findings demonstrate the utility of our computational methods to uncover functional residues on proteins based on fragment-binding sites
Crystallographic characterisation and development of bi-substrate inhibitors of coronavirus nsp14 methyltransferase
Get PDFSARS-CoV-2 non-structural protein 14 (nsp14) is essential for viral mRNA cap guanine-N7 methylation and represents a promising but underexplored antiviral target. Herein we describe a structure-guided campaign based on a hit from a focussed SAM mimetic library. Systematic SAR exploration guided by six X-ray co-crystal structures in complex with SARS-CoV-2 led to compound 26, a bi-substrate inhibitor that bridges the SAM and RNA cap binding sites. Compound 26 achieved nanomolar potency against nsp14 from SARS-CoV-2 (IC₅₀ = 53 nM), SARS-CoV-1, and two alphacoronaviruses, with excellent selectivity over human RNMT and flaviviral MTase. In general, the compounds demonstrated favourable metabolic stability, passive permeability, and no HepG2 cytotoxicity. However, cellular antiviral activity was limited, revealing disconnects between enzyme inhibition and phenotypic response. These findings provide a structural framework for optimizing bi-substrate methyltransferase inhibitors against coronaviruses with a view for pan-coronaviral activity
Assessing climate change risks to dissolved organic carbon concentrations in drinking water sources using a catchment sensitivity approach
No full textOver recent decades, dissolved organic carbon (DOC) concentrations in rivers have increased across the Northern Hemisphere. Climate change contributes to altered thermal, hydrological, and biogeochemical processes. Understanding climatic drivers of DOC production and export is crucial to project future water discolouration and disinfection by-product formation risks. Using raw (untreated) water data from 127 Scottish river and lake catchments, carbon concentrations were related to climate parameters. Based on correlations of total organic carbon (as proxy for DOC), rainfall and temperature data, five “sensitivity” categories were identified. Climate projections using UKCP18 (2041–2060) enabled the identification of those catchments most susceptible to future DOC losses and the prioritisation of anticipatory adaptation actions. Catchments in southeast Scotland were particularly highlighted as at-risk due to summer rainfall reductions. This analysis supports an understanding of climate change risks and informs policy development on land management, water treatment investment, and adaptive management to maintain pre-treatment water quality and ecosystem resilience
HaDM-ST:Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation
No full textSpatial transcriptomics (ST) reveals spatial heterogeneity of gene expression, yet its resolution is limited by current platforms. Recent methods enhance resolution via H&E-stained histology, but three major challenges persist: (1) isolating expression-relevant features from visually complex H&E images; (2) achieving spatially precise multimodal alignment in diffusion-based frameworks; and (3) modeling gene-specific variation across expression channels. We propose HaDM-ST (Histology-assisted Differential Modeling for ST Generation), a high-resolution (HR) ST generation framework conditioned on H&E images and low-resolution (LR) ST. HaDM-ST includes: (i) a semantic distillation network to extract predictive cues from H&E; (ii) a spatial alignment module enforcing pixel-wise correspondence with low-res ST; and (iii) a channel-aware adversarial learner for fine-grained gene-level modeling. Experiments on 200 genes across diverse tissues and species show HaDM-ST consistently outperforms prior methods, enhancing spatial fidelity and gene-level coherence in HR ST predictions.</p
TamilSandhi:A Neuro-Symbolic AI Toolkit for Correcting Sandhi Errors in Tamil
No full textTamil is an agglutinative and highly inflectional language with rich morphology. Sandhi errors are a class of spelling mistakes in Tamil that occur at word boundaries due to incorrect insertion or omission of hard consonants. This paper presents TamilSandhi, an open-source neuro-symbolic framework for identifying and correcting such errors. It combines rule-based logic for simple Sandhi rules with neural models for one complex rule. The rule-based system implements 12 Vallinam (hard consonant) addition rules and 8 deletion rules, derived from classical Tamil grammar texts such as Tolkappiyam and Nannool. These rules were packaged into a PyPi library (https://pypi.org/project/tamilsandhi-toolkit/) and validated using a suite of 300 unit tests (235 for addition, 65 for deletion). To address a complex rule not covered by the rule-based logic, a neural sequence-to-sequence approach was used. A supervised corpus of 10,434 manually annotated sentence pairs, each consisting of an incorrect and corrected version, was created. Three multilingual transformer models—mBART, mT5, and NLLB—were fine-tuned. Among them, mBART achieved the best performance, with a BLEU score of 99.9 and exact match accuracy of 97.9%.TamilSandhi is released as an open-source project on GitHub (https://github.com/TamilGeekGirl/TamilSandhiNeuroSymbolicAI/). Due to its modularity, reproducibility, and linguistic validity, TamilSandhi constitutes a significant contribution to NLP research in a widely used but low-resource language.</p
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