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T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
No full textCropland cultivation is fundamental to food security and plays a crucial role in the global water, energy, and carbon cycles. However, our understanding of how climate change will impact cropland functions is still limited. This knowledge gap is partly due to the simplifications made in terrestrial biosphere models (TBMs), which often overlook essential agricultural management practices such as irrigation and fertilizer application and simplify critical physiological crop processes. Here, we demonstrate how, with minor, parsimonious enhancements to the TBM T&C, it is possible to accurately represent a complex cropland system. Our modified model, T&C-CROP, incorporates realistic agricultural management practices, including complex crop rotations and irrigation and fertilization regimes, along with their effects on soil biogeochemical cycling. We successfully validate T&C-CROP across four distinct agricultural sites, encompassing diverse cropping systems such as multi-crop rotations, monoculture, and managed grassland. A comprehensive validation of T&C-CROP was conducted, encompassing water, energy, and carbon fluxes; leaf area index (LAI); and organ-specific yields. Our model effectively captured the heterogeneity in daily land surface energy balances across crop sites, achieving coefficients of determination of 0.77, 0.48, and 0.87 for observed versus simulated net radiation (Rn), sensible heat flux (H), and latent heat flux (LE), respectively. Seasonal, crop-specific gross primary production (GPP) was simulated with an average absolute bias of less than 10 %. Peak-season LAI was accurately represented, with an r2 of 0.67. Harvested yields (above-ground biomass, grain, and straw) were generally simulated within 10 %–20 % accuracy of observed values, although inter-annual variations in crop-specific growth were difficult to capture
SARS-CoV-2 variants retain high airborne transmissibility by different strategies
No full textSARS-CoV-2 variants evolve to balance immune evasion and airborne transmission, yet the mechanisms remain unclear. In hamsters, first-wave, Alpha, and Delta variants transmitted efficiently via aerosols. Alpha emitted fewer viral particles than first-wave virus but compensated with a lower infectious dose (ID50). Delta exhibited higher airborne emission but required a higher ID50. A fall in airborne emission of infectious Delta virus over time after infection correlated with a decrease in its infectivity to RNA ratio in nasal wash and a decrease in contagiousness to sentinel animals. Omicron subvariants (BA.1, EG.5.1, BA.2.86, JN.1) displayed varying levels of airborne transmissibility, partially correlated with airborne emissions. Mutations in the non-spike genes contributed to reduced airborne transmissibility, since recombinant viruses with spike genes of BA.1 or JN.1 and non-spike genes from first-wave virus are more efficiently transmitted between hamsters. These findings reveal distinct viral strategies for maintaining airborne transmission. Early assessment of ID50 and aerosolized viral load may help predict transmissibility of emerging variants
Boundary objects as catalysts for creative thinking in adolescent education
No full textCreative thinking has become more important in education globally due to industry demand and a fast-paced world. Boundary objects that can be tangible or digital objects are investigated to understand their role in facilitating creative thinking across 5 subject areas for teenagers aged 13-18 and their teachers, in their natural learning environment. A multiple case study method is used to investigate learners’ and their teachers’ experience in using boundary objects, to enable communication and understanding between individuals or groups in learning. Participants from an inner London secondary school comprised case groups: 8 Teachers and 16 Learners (8 from the lower school, aged 13-15 years, and 8 from the upper school, aged 16-18 years). Participants were invited through email and a short presentation. Consented participants were organised into male and female across teachers and students and were approached in lessons where boundary objects were being used. Data was collected through interviews and comprised photos of tool use, analysed through
Reflexive Thematic Analysis for data analysis. The resulting five themes for teacher and student themes showed that boundary objects were perceived to facilitate creative thinking across all case groups within the studied context, with important insights such as iterative design which develops real-world skills; metacognition which is critical in learning that enables students to actively question their own thinking; memory which is very important in enabling students to remember what they learned and how; individual liberty suggesting that learning need not be linear nor prescribed but with freedom to learn in ways that are enjoyable and challenging too, amongst others. The study’s interpretive results indicate that when participants experience the use of boundary objects in a
natural classroom or learning setting, the learning process is perceived to bring benefits that allow for the process of creative thinking to occur
Reduced aerosol pollution diminished cloud reflectivity over the North Atlantic and Northeast Pacific
No full textOver the past several decades, the proportion of solar radiation reflected back into space has declined, accelerating the accumulation of heat within the Earth system. Here we show that the marine cloud reflectivity decreased on average by 2.8 ± 1.2% per decade in the combined North Atlantic and Northeast Pacific regions between 2003 and 2022. The majority of the Earth System Models we analyzed simulated a significantly weaker cloud reflectivity decrease and warming of the sea surface in these regions than observed. In contrast, our simulations using an improved aerosol-climate model reproduce the spatial extent and magnitude of the observed cloud reflectivity decrease. We show that reductions in sulfur dioxide and other aerosol precursors accounted for 69% (range 55−85%) of the cloud reflectivity decrease through aerosol-cloud interactions, consistent with the observed aerosol and cloud trends. This raises the prospect of a continuing cloud reflectivity decrease and an associated warming impact in these regions, given that the emission reductions are projected to persist over the next few decades. Further research is needed to assess whether near-term climate scenarios should be revised to account for the weak cloud reflectivity reductions in the Earth System Models
Towards robust and reliable disease classification in medical imaging
No full textIn healthcare, machine learning systems have the potential to improve clinical workflows, leading to better patient outcomes, shortened waiting times, and reduced health inequalities. Such systems have already achieved human-level performance in numerous challenging image-based disease detection tasks. Nonetheless, no model is error-free. Hence, for the safe and reliable use of machine learning in clinical practice, there is a critical need to establish safeguards for error detection and to ensure models work robustly across different environments. This is the purpose of this thesis.
In the first part, we focus on improving the reliability of image-based disease classification models. Specifically, we study the detection of misclassified samples, and introduce novel methods for automated performance estimation and dataset shift identification. This research is key for developing systems that alert clinicians when machine learning models fail. In the second part, we address model robustness, focusing on developing novel methods to ensure predictions remain accurate regardless of changes in image acquisition protocols. On the one hand, we propose an unsupervised recalibration method to automatically correct clinical metric drifts induced by acquisition shifts at test-time, for already trained models. On the other hand, we propose counterfactual contrastive learning, a novel framework leveraging causal generative modelling to enhance the robustness of contrastively-learned image representations.
Overall, by developing new methods to (i) improve reliability and performance monitoring of disease classification models and (ii) enhance model robustness against image acquisition shifts, this thesis takes important steps towards enabling the safe deployment of machine learning systems in medical imaging.Open Acces
Tailored heat treatments to characterise the fracture resistance of critical weld regions in hydrogen transmission pipelines
No full textA new protocol is presented to directly characterise the toughness of microstructural regions present within the weld heat-affected zone (HAZ), the most vulnerable location governing the structural integrity of hydrogen transport pipelines. Heat treatments are tailored to obtain bulk specimens that replicate predominantly ferritic-bainitic, bainitic, and martensitic microstructures present in the HAZ. These are applied to a range of pipeline steels to investigate the role of manufacturing era (vintage versus modern), chemical composition, and grade. The heat treatments successfully reproduce the hardness levels and microstructures observed in the HAZ of existing natural gas pipelines. Subsequently, fracture experiments are conducted in air and pure H at 100 bar, revealing a reduced fracture resistance and higher hydrogen embrittlement susceptibility of the HAZ microstructures, with initiation toughness values as low as 32 MPa. The findings emphasise the need to adequately consider the influence of microstructure and hard, brittle zones within the HAZ
Challenges in respiratory medicine: the need for integrated tuberculosis and respiratory care in low-resource settings
No full textBackground Pulmonary tuberculosis (PTB) and chronic respiratory diseases (CRDs) are intricately linked. People with PTB and CRDs experience similar symptoms, including breathlessness, cough and chest pain. They may have similar risk factors for disease, including smoking and occupational exposures. PTB is also a direct cause of lung damage in the form of post-TB lung disease. However, despite the overlap in risk factors, symptoms and sequelae, public health and clinical care pathways for TB and CRDs remain almost entirely separate in many low- and middle-income countries (LMICs). Those with respiratory symptoms are directed to TB services as a first point of contact where they are known as ‘people with presumptive TB’, and pathways to respiratory diagnosis and care remain largely inadequate.
Aim In this opinion piece we describe opportunities for the integration of tuberculosis (TB) and respiratory care, as a means of improving patient outcomes in LMICs. Strategies may include upstream public health interventions to address shared risk factors, the use of shared diagnostic pathways, the provision of decentralised access to both TB and CRD care, and coordinated information provision about the risk factors and symptoms of both conditions. Health-related benefits may include more timely diagnosis of CRDs, improved CRD treatment and care, and reduced inappropriate empirical TB treatment or retreatment. We highlight the need for pilot models of integrated care, with robust design and evaluation, and we note that an integrated approach may be particularly timely given the increasing scarcity of global health donor funding
Extended susceptibility testing for refractory Helicobacter pylori infection: regional testing should guide antimicrobial decision making
No full textBackground: Helicobacter pylori (H. pylori) is a Gram-negative bacterium and common cause of gastritis. Antimicrobial treatment typically involves two agents and is prescribed empirically however therapy can be complicated by drug allergies or previous, unsuccessful regimens. Recent data from Europe suggests rising resistance to commonly used agents but contemporary data relevant to UK populations, particularly following the COVID-19 pandemic is limited. This study aimed to report susceptibility testing results in refractory cases of H. pylori infections to evaluate local resistance patterns, inform treatment strategies, and compare findings with data from the European Registry on H. pylori management.
Methods: A retrospective multi-centre cohort study was conducted between September 2018 and September 2023 at North West London Pathology (London, UK), a central laboratory operating through a hub and spoke model, to assess extended antimicrobial susceptibilities in gastric biopsy samples from patients with refractory H. pylori infection. Antimicrobial susceptibilities were assessed using minimum inhibitory concentration methods as per contemporaneous European Committee on Antimicrobial Susceptibility Testing guidelines. Results were compared with European data.
Results: A total of 193 individual isolates were identified. Mean resistance rates were low for tetracycline (2.3%) and amoxicillin (7.3%), moderately low for rifampicin (14.0%), moderate for levofloxacin (27.3%) and high for metronidazole (82.7%) and clarithromycin (75.5%) across the study period. Levofloxacin had a trend of increasing susceptibility (p=0.10) and rifampicin of increasing resistance (p=0.31) throughout the study. Resistance rates were significantly higher for the non-naïve North West London cohort compared with the European non-naïve cohort for metronidazole (p<0.001), amoxicillin (p<0.01), and clarithromycin (p=0.02).
Conclusion: These findings emphasize the necessity of tailored treatment approaches, informed by regional susceptibility patterns. As antimicrobial resistance continues to evolve a proactive and adaptive approach to treatment strategies remains paramount to effectively treat H. pylori infection and mitigate associated clinical and financial burdens
Paving the way for incumbents' digital transformation. A review and research agenda
No full textDigital transformation is reshaping the competitive landscape by forcing incumbent firms to rethink their strategies, organizational structures, and business models. While a substantial body of literature has explored digital transformation in specific sectors, focusing on various factors and organizational mechanisms, there remains a lack of a comprehensive and cohesive understanding of how incumbent firms actively lead or respond to these transformations. As a result, the concept remains somewhat fragmented and underdeveloped. This review addresses this gap by conducting a systematic review of 68 peer-reviewed articles across five major academic domains: entrepreneurship, general management, innovation, organization studies, and strategy. Our review identifies pathways of leading versus responding to digital transformation as well as the internal and external consequences and antecedents that enable or constrain digital transformation. We also offer a research agenda aimed at deepening our theoretical and managerial understanding of how incumbent firms navigate digital transformation, providing novel directions for future studies
The effect of clinically relevant changes in extracellular electrolyte concentrations on human atrial arrhythmias
No full textBackground
Patients with recent-onset atrial fibrillation (AF) frequently present with plasma electrolyte imbalances. Low plasma concentrations of potassium (hypokalaemia), and more recently low plasma concentrations of sodium (hyponatremia), have both been shown to contribute to a pro-arrhythmic substrate and may affect the success of restoring the normal rhythm (cardioversion). However, the mechanistic effects of these electrolyte alterations on atrial electrophysiology remain incompletely understood. This study aims to investigate how clinically relevant variations in extracellular electrolyte concentrations influence human atrial electrical activity and arrhythmia initiation.
Methods
We applied our cardiac digital twin methodology to a cohort of 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent). For each patient-specific model, we simulated sinus rhythm and AF induction under baseline and 30 distinct combinations of extracellular potassium, sodium and calcium concentrations. Global sensitivity analysis and machine learning were used to quantify how these electrolyte alterations affect key electrophysiological markers, including action potential duration, resting membrane potential (RMP), and conduction velocity (CV), and the induction and maintenance of AF.
Results
Here we show that our computational framework accurately replicates the experimentally observed sensitivity of human atrial electrophysiology to electrolyte variations. Hyponatremia significantly modifies the action potential waveform, thereby promoting AF sustainability, while hypokalaemia predominantly alters the RMP and thus CV, and only moderately increases AF inducibility.
Conclusions
The combination of clinical data sets and multiscale computational analyses yields insights into cellular and tissue-level mechanisms for AF as well as suggesting personalised approaches for management and treatment