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Casting a wider net on ocean NETs
No full textSocietal issues involving policies and publics are generally understudied in research on ocean-based Negative Emission Technologies (NETs), yet will be crucial if novel techniques are ever to function at scale. Publics influence political mandates and market uptake, and are key to robust decision-making and responsible incentivisation. Discourses surrounding ocean NETs will also have fundamental effects on governance for the techniques, shaping how they are defined, who is assigned the authority to govern, and what instruments are deemed appropriate. This Perspective brings together key insights on the societal dimensions of ocean NETs, including public acceptability, policy assessment, governance and discourse. Ocean iron fertilisation is the only ocean NET on which there exists considerable social science research, and we show that much evidence points against its social desirability. This, together with considerable natural science uncertainties, leads us to question whether further research is actually necessary to rule out ocean iron fertilisation as an option. For other ocean NETs, there are considerable knowledge gaps, yet the available evidence suggests that the majority of ocean NETs may face a greater public acceptability challenge than terrestrial NETs. Ocean NETs also raise complex governance questions which go well beyond the remit of natural sciences and engineering. Using a conceptual exploration of the ways in which different types of discourse may shape emerging ocean NETs governance, we show that the very idea of ocean NETs is likely to set the stage for a whole new range of contested futures
An Evidential Reasoning Rule Based Feature Selection for Improving Trauma Outcome Prediction
No full textVarious demographic and medical factors can be linked to severe deterioration of patients suffering from traumatic injuries. Accurate identification of the most relevant variables is essential for building more accurate prediction models and making more rapid life-saving medical decision. The intention of this paper is to select a number of features that can be used to accurately predict patients’ outcomes through three feature selection methods: random forest, ReliefF and the evidential reasoning (ER) rule. The impact of an outcome’s class imbalance on feature selection is discussed, and synthetic minority over-sampling technique (SMOTE) is performed to show the differences in the selected features. The results show that length of stay in hospital, length of stay in intensive care unit, age and Glasgow Coma Scale (GCS) are the most selected features across different techniques. The prediction models based on the features selected by the ER rule show the highest prediction performance represented by the area under the receiver operating characteristic curve (AUC) values, which has a median of 0.895 for the model employed by the ten highest-weighted variables, while the median AUC values are 0.827 and 0.885 if the ten highest-weighted variables are selected by ReliefF and random forest respectively. The results also show that after the ten most important features, increasing the number of the less important features has only a slight increase in prediction accuracy
Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk
No full textPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestries; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestries; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestries; hazard ratios of 1.36(95%CI:1.29–1.43,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.49(95%CI:1.35-1.64,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs
Mineral diagenesis and inferred fluids in basinal mudstones: the Carboniferous Morridge Formation, Widmerpool Gulf, UK
No full textAn appreciation of the diagenetic processes impacting basinal mudstones is important for understanding the properties of mudstones and the nature of fluids which might have been expelled onto adjacent carbonate platforms. This study utilizes petrographic and mineralogical data from mudstones from the Serpukhovian Morridge Formation in the Widmerpool Gulf, UK to investigate the paragenetic succession and associated mineral precipitation during diagenesis of mudstones. Authigenic minerals in the studied samples contain kaolinite, calcite, non-ferroan and ferroan dolomite, pyrite, ankerite, and quartz. The Si/Al ratios in most samples are above 2.5, indicating that most of the studied samples contain excess silica. The widespread presence of authigenic kaolinite and quartz provides important information on the sources for dissolved silica in pore water. The presence of pyritized radiolarians indicates that the dissolution of siliceous radiolarian skeletons is the most likely principle silica source within the system. The illitization of kaolinite also provided a possible silica source for quartz overgrowth during burial diagenesis. It has been long-proposed that the supply of base metals (i.e. Ba, Zn and Mg) for mineralization on the adjacent Derbyshire Platform came from juxtaposed basins, but evidence for this has been limited. The results of this study show that the diagenetic evolution of mudstones in the late Carboniferous links closely to the mineralization observed on the Derbyshire Platform. In the Widmerpool Gulf, the source of magnesium incorporated into dolomite is likely from basinal brine that also provided a source of barium for the barite mineralization. Based on the burial history model, the studied samples were buried to 1.3 km with a temperature of 82℃ in the late Carboniferous. Hydrocarbon was generated during maturation of organic matter and migrated with basinal brines through factures into surrounding formations during basin inversion. This mixed fluid likely contributed to the mineralization on the Derbyshire Platform
Exploiting the SAT Revolution for Automated Software Verification: Report from an Industrial Case Study
No full textIn the last three decades, Boolean Satisfiability (SAT) solvers experienced a dramatic performance revolution; they are now used as the backend of various industrial verification engines. SAT solvers can now check logical formulas that contain millions of propositional variables. In Satisfiability Modulo Theories (SMT) solvers, predicates from various theories are not encoded using propositional variables as in SAT but remain in the problem formulation. Thus, SMT solvers can be used as backends for solving the generated verification conditions to cope with increasing software complexity from industrial applications. This talk will overview automated software verification techniques that rely on sophisticated SMT solvers built over efficient SAT solvers. I will discuss challenges, problems, and recent advances to ensure safety and security in opensource and embedded software applications. I will describe novel algorithms that exploit fuzzing, explicit-state, and SMT-based symbolic model checking for verifying single- and multi-threaded software. These algorithms were the first to verify multi-threaded C/Posix software based on shared-memory synchronization and communication symbolically. They are implemented in industrial strength software verification tools, now considered state-of-the art in the software testing and verification community, receiving 28 medals at SV-COMP and Test-COMP. This achievement enabled industrial research collaborations with Intel and Nokia. Software engineers applied these tools to find real security vulnerabilities in large-scale software systems (e.g., memory safety in firmware for Intel and arithmetic overflow in telecommunication software for Nokia, neither of which had been found before)
Everolimus-induced pneumonitis in patients with neuroendocrine neoplasms: real-world study on risk factors and outcomes
No full textBackground: Everolimus-induced pneumonitis (EiP) has been poorly studied in patients with neuroendocrine neoplasms (NEN) outside clinical trials. The aim of this study was to evaluate the incidence, risk factors, and outcomes of EiP in patients with NENs using real-world data. Methods: Retrospective study of everolimus-treated patients with advanced NENs. Imaging reports were systematically reviewed for the presence of pneumonitis. Clinical features and treatment profiles for EiP were summarized. Overall survival (OS) was calculated from the initiation of everolimus to the date of death or last follow-up using the Kaplan-Meier method. Results: A total of 122 patients were included. Median age at start of everolimus was 62 (19-86) years, 62% (76/122) were male, and half were from pancreatic origin (62, 51%). Twenty-eight patients (23%) developed EiP: 82% grade (G)1 or G2, 14% G3 and 4% G4. The median time to EiP was 3.6 (0.8-51) months. Primary tumor site, concurrent lung disease, smoking history and prior therapies were not associated with the onset of EiP. Patients who developed EiP had longer time on everolimus treatment (median 18 months vs 6 months; p = .0018) and OS (77 months vs 52 months; p = .093). EiP was a predictor of improved OS by multivariable analysis (HR 0.39, 95% CI 0.19-0.82; p = .013). Conclusion: EiP in the real-world clinical setting is present in one quarter of patients with NENs receiving everolimus and often occurs early. While risk factors for EiP were not identified, patients with EiP had improved survival.<br/
IPIP27A cooperates with OCRL to support endocytic traffic in the zebrafish pronephric tubule
No full textEndocytosis is a fundamentally important process through which material is internalized into cells from the extracellular environment. In the renal proximal tubule, endocytosis of the abundant scavenger receptor megalin and its co-receptor cubilin play a vital role in retrieving low molecular weight proteins from the renal filtrate. Although we know much about megalin and its ligands, the machinery and mechanisms by which the receptor is trafficked through the endosomal system remain poorly defined. In this study, we show that Ipip27A, an interacting partner of the Lowe syndrome protein OCRL, is required for endocytic traffic of megalin within the proximal renal tubule of zebrafish larvae. Knockout of Ipip27A phenocopies the endocytic phenotype seen upon loss of OCRL, with a deficit in uptake of both fluid-phase and protein cargo, which is accompanied by a reduction in megalin abundance and altered endosome morphology. Rescue and co-depletion experiments indicate that Ipip27A functions together with OCRL to support proximal tubule endocytosis. The results therefore identify Ipip27A as a new player in endocytic traffic in the proximal tubule in vivo and support the view that defective endocytosis underlies the renal tubulopathy in Lowe syndrome and Dent-2 disease
Interfered-Naming Therapy for Aphasia (INTA): Behavioural and computational effects of a novel linguistic-executive approach
No full textBackground and aims. Executive functions recently have gained attention as important contributor to language performance in aphasia. Picture/word interference embraces both executive control and lexical processing through distracted confrontation naming. Thus, we created a novel approach that embedded interfered-naming into an established lexical therapy framework. We aimed to (1) investigate patients’ behavioural response to distractor types and treatment methods of interfered-naming and (2) determine specific therapy effects on linguistic versus executive processing and initial eligibility criteria. Methods. Persons with word finding difficulties in chronic aphasia received 4-weeks therapy in a block design with thorough pre-post-testing including computational modelling. During therapy, picture naming was distracted by auditory stimuli which were primed by a preceding comprehension task and directly assisted by increasing semantic or phonological cues. Results. 19 participants were included into the diagnostic study, 12 of which also completed the therapy study. Distractor types did not generally yield differential effects at baseline. The novel linguistic-executive treatment significantly improved pure naming in most (9 out of 12) cases, fostered generalisation to untrained items, increased semantic weights in the computational model and reduced automated speech. Therapy gains correlated positively with initial distractor comprehension, lexical-semantics and word discrimination, and negatively with automated speech and conceptual-semantics. Conclusion. The interference paradigm combined with computational modelling offers a useful tool for aphasia diagnosis and the new treatment approach revealed to be effective. Semantic and executive processing appear to be the core source of improvements
A 15.5 GHz detection of the galaxy cluster minihalo in RXJ1720.1+2638
No full textRXJ1720.1¸2638 is a cool-core, ‘relaxed-appearing’ cluster with a minihalo previously detected up to 8.4 GHz, confined by X-ray-detected cold fronts. We present observations of the minihalo at 13 – 18 GHz with the Arcminute Microkelvin Imager telescope, simultaneously modelling the Sunyaev–Zel’dovich signal of the cluster in conjunction with Planck and Chandra data in order to disentangle the non-thermal emission of the minihalo. We show that the previously-reported steepening of the minihalo emission at 8.4 GHz is not supported by the AMI data and that the spectrum is consistent with a single power-law up to 18 GHz. We also show the presence of a larger-scale component of the minihalo extending beyond the cold fronts. Both of these observations could be explained by the ‘hadronic’ or ‘secondary’ mechanism for the production of relativistic electrons, rather than the currently-favoured ‘re-acceleration’ mechanism and/or multiple episodes of jet activity from the active galactic nucleus in the brightest cluster galaxy
Local charge distributions, electric dipole moments and local electric fields influence reactivity patterns and guide regioselectivities in α-ketoglutarate-dependent nonheme iron dioxygenases.
No full textNonheme iron dioxygenases catalyze vital processes for human health related to the biosynthesis of essential products and the biodegradation of toxic metabolites. Often the natural product biosynthesis by these nonheme iron dioxygenases is highly regio- and chemoselective, which are commonly assigned to tight substrate binding and positioning. However, recent highlevel computational modeling has shown that substrate binding and positioning is only part of the story and long-range electrostatic interactions can play a major additional role. In this Account, we review and summarize computational viewpoints on the high regioand chemoselectivity of α-ketoglutarate-dependent nonheme iron dioxygenases and how external perturbations affect the catalysis. In particular, studies from our groups have shown that often a regioselectivity in enzymes can be accomplished by external perturbations working on the transition state for the reaction through external charges, electric dipole moments or local electric field effects.Furthermore, bond dissociation energies in molecules are shown to be influenced by an electric field effect; and through targeting a specific bond in an electric field this can lead to an unusual specificity reaction. For instance, in the carbon-induced starvation protein, we studied two substrate-bound conformations and showed that regardless of what C-H bond of the substrate is closest to the iron(IV)-oxo oxidant, the lowest hydrogen atom abstraction barrier is always for the pro-S C²-H abstraction due to an induced dipole moment of the protein that weakens this bond. In another example on the hygromycin biosynthesis enzyme, an oxidative ring-closure reaction in the substrate forms an ortho-δ-ester ring. Calculations on this enzyme show that the selectivity is guided by a protonated lysine residue in the active site that, through its positive charge, triggers a low energy hydrogen atom abstraction barrier. A final set of examples in this Account discuss the viomycin biosynthesis enzyme and the 2-(trimethylammonio)ethylphosphonate dioxygenase (TmpA) enzymes. Both of these enzymes are shown to possess a significant local electric dipole moment and local electric field effect due to charged residues surrounding the substrate and oxidant binding pocket. This dipole moment and local electric field strength changes the C-H bond strengths of the substrate and triggers the regioselectivity of substrate activation. In particular, we show that in the gas-phase and in an enzyme environment C-H bond strengths vary due to local electric dipole moments and electric field strengths. These examples show that enzymes have an intricately designed structure that enables a chemical reaction under ambient conditions through the positioning of positively and negatively charged residues that influence and enhance reaction mechanisms. These computational insights create huge possibilities in bioengineering to apply local electric field and dipoles in proteins to achieve an unusual reaction selectivity and trigger a fit-for-purpose biocatalyst for unique biotransformations