Learning together: Lessons from a collaborative curriculum design project

Based on an action research project implemented at two South African universities, we argue that content and language integration (ICL) collaborative partnerships benefit not only from collaboration between language and content specialists, but in addition, from collaboration between language specialists, general education specialists and content specialists from a variety of disciplines. However, as we illustrate below, these benefits may be accompanied by substantial challenges. We make a further claim, for the value of a transformative approach towards collaboration for content and language integration, in which the teacher/researchers engage in their practice in a critical and reflexive manner, and by so doing, foster their own deep learning, as well as the deep learning of the students

Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Framework

Financial cybercrime prevention is an increasing issue with many organisations and governments. As deep learning models have progressed to identify illicit activity on various financial and social networks, the explainability behind the model decisions has been lacklustre with the investigative analyst at the heart of any deep learning platform. In our paper, we present a state-of-the-art, novel multimodal proactive approach to addressing XAI in financial cybercrime detection. We leverage a triad of deep learning models designed to distill essential representations from transaction sequencing, subgraph connectivity, and narrative generation to significantly streamline the analyst's investigative process. Our narrative generation proposal leverages LLM to ingest transaction details and output contextual narrative for an analyst to understand a transaction and its metadata much further.Comment: 6 pages, 3 figure

Hsp21potentiates antifungal drug tolerance in Candida albicans

Peer reviewedPublisher PD

Reduction of prevalence of patients meeting the criteria for metabolic syndrome with tirzepatide: a post hoc analysis from the SURPASS Clinical Trial Program

Background: Metabolic syndrome is characterized as the co-occurrence of interrelated cardiovascular risk factors, including insulin resistance, hyperinsulinemia, abdominal obesity, dyslipidemia and hypertension. Once weekly tirzepatide is approved in the US and EU for the treatment of type 2 diabetes (T2D) and obesity. In the SURPASS clinical trial program for T2D, tirzepatide demonstrated greater improvements in glycemic control, body weight reduction and other cardiometabolic risk factors versus placebo, subcutaneous semaglutide 1 mg, insulin degludec, and insulin glargine. This post hoc analysis assessed the effect of tirzepatide use on the prevalence of patients meeting the criteria for metabolic syndrome across SURPASS 1–5. Methods: Metabolic syndrome was defined as having ≥ 3 of 5 criteria according to the US National Cholesterol Education Program: Adult Treatment Panel III. Analyses were based on on-treatment data at the primary endpoint from patients adherent to treatment (taking ≥ 75% study drug). A logistic regression model with metabolic syndrome status as the response variable, metabolic syndrome status at the baseline visit as an adjustment, and randomized treatment as fixed explanatory effect was used. The effect of tirzepatide use on the prevalence of patients meeting the criteria for metabolic syndrome by categorical weight loss, background medication and gender were assessed. Results: In SURPASS, the prevalence of patients meeting the criteria for metabolic syndrome at baseline was 67–88% across treatment groups with reductions at the primary endpoint to 38–64% with tirzepatide versus 64–82% with comparators. Reductions in the prevalence of patients meeting the criteria for metabolic syndrome was significantly greater with all tirzepatide doses versus placebo, semaglutide 1 mg, insulin glargine, and insulin degludec (p < 0.001). Individual components of metabolic syndrome were also reduced to a greater extent with tirzepatide vs comparators. Greater reductions in body weight were associated with greater reductions in the prevalence of patients meeting the criteria for metabolic syndrome and its individual components. Background SGLT2i or sulfonylurea use or gender did not impact the change in prevalence of patients meeting the criteria for metabolic syndrome. Conclusions: In this post hoc analysis, tirzepatide at all doses studied was associated with a greater reduction in the prevalence of patients meeting the criteria for metabolic syndrome compared to placebo, semaglutide 1 mg, insulin degludec, and insulin glargine. Although more evidence is needed, these data would support greater potential improvement in cardiovascular risk factor profile with tirzepatide treatment in people across the continuum of T2D

WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe

Abstract. Chemical modelling studies have been conducted over north-western Europe in summer conditions, showing that night-time dinitrogen pentoxide (N2O5) heterogeneous reactive uptake is important regionally in modulating particulate nitrate and has a~modest influence on oxidative chemistry. Results from Weather Research and Forecasting model with Chemistry (WRF-Chem) model simulations, run with a detailed volatile organic compound (VOC) gas-phase chemistry scheme and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) sectional aerosol scheme, were compared with a series of airborne gas and particulate measurements made over the UK in July 2010. Modelled mixing ratios of key gas-phase species were reasonably accurate (correlations with measurements of 0.7–0.9 for NO2 and O3). However modelled loadings of particulate species were less accurate (correlation with measurements for particulate sulfate and ammonium were between 0.0 and 0.6). Sulfate mass loadings were particularly low (modelled means of 0.5–0.7 μg kg−1air, compared with measurements of 1.0–1.5 μg kg−1air). Two flights from the campaign were used as test cases – one with low relative humidity (RH) (60–70%), the other with high RH (80–90%). N2O5 heterogeneous chemistry was found to not be important in the low-RH test case; but in the high-RH test case it had a strong effect and significantly improved the agreement between modelled and measured NO3 and N2O5. When the model failed to capture atmospheric RH correctly, the modelled NO3 and N2O5 mixing ratios for these flights differed significantly from the measurements. This demonstrates that, for regional modelling which involves heterogeneous processes, it is essential to capture the ambient temperature and water vapour profiles. The night-time NO3 oxidation of VOCs across the whole region was found to be 100–300 times slower than the daytime OH oxidation of these compounds. The difference in contribution was less for alkenes (× 80) and comparable for dimethylsulfide (DMS). However the suppression of NO3 mixing ratios across the domain by N2O5 heterogeneous chemistry has only a very slight, negative, influence on this oxidative capacity. The influence on regional particulate nitrate mass loadings is stronger. Night-time N2O5 heterogeneous chemistry maintains the production of particulate nitrate within polluted regions: when this process is taken into consideration, the daytime peak (for the 95th percentile) of PM10 nitrate mass loadings remains around 5.6 μg kg−1air, but the night-time minimum increases from 3.5 to 4.6 μg kg−1air. The sustaining of higher particulate mass loadings through the night by this process improves model skill at matching measured aerosol nitrate diurnal cycles and will negatively impact on regional air quality, requiring this process to be included in regional models. This work was supported by the NERC RONOCO project NE/F004656/1. S. Archer-Nicholls was supported by a NERC quota studentship.This is the final version of the article. It first appeared at http://www.atmos-chem-phys.net/15/1385/2015/acp-15-1385-2015.pd

Basalt-trachybasalt samples in Gale Crater, Mars

The ChemCam instrument on the Mars Science Laboratory (MSL) rover, Curiosity, observed numerous igneous float rocks and conglomerate clasts, reported previously. A new statistical analysis of single-laser-shot spectra of igneous targets observed by ChemCam shows a strong peak at ~55 wt% SiO2 and 6 wt% total alkalis, with a minor secondary maximum at 47–51 wt% SiO2 and lower alkali content. The centers of these distributions, together with the rock textures, indicate that many of the ChemCam igneous targets are trachybasalts, Mg#=27 but with a secondary concentration of basaltic material,with a focus of compositions around Mg#=54. We suggest that all of these igneous rocks resulted from low-pressure, olivine-dominated fractionation of Adirondack (MER) class-type basalt compositions. This magmatism has subalkaline, tholeiitic affinities. The similarity of the basalt endmember to much of the Gale sediment compositions in the first 1000 sols of the MSL mission suggests that this type of Fe-rich, relatively low-Mg#, olivine tholeiite is the dominant constituent of the Gale catchment that is the source material for the fine-grained sediments in Gale. The similarity to many Gusev igneous compositions suggests that it is a major constituent of ancient Martian magmas, and distinct from the shergottite parental melts thought to be associated with Tharsis and the Northern Lowlands. The Gale Crater catchment sampled a mixture of this tholeiitic basalt along with alkaline igneous material, together giving some analogies to terrestrial intraplate magmatic provinces

Adaptation to multi-meter sea-level rise should start now

Sea-level rise will fundamentally change coastal zones worldwide (Cooley et al 2022). A global two meters rise of sea level will be exceeded sooner or later within a time window ranging from one century to as long as two millennia, depending on future greenhouse gas emissions and polar ice-sheet melting (Fox-Kemper et al 2021). Here, we show that in addition to climate mitigation to slow this rise, adaptation to two meters of sea-level rise should start now. This involves changing our mindset to define a strategic vision for these threatened coastal areas and identify realistic pathways to achieve this vision. This can reduce damages, losses, and lock-ins in the future, identify problems before they become critical and exploit opportunities if they emerge. To meet this challenge, it is essential that coastal adaptation becomes core to coastal development, especially for long-lived critical infrastructure. Coastal adaptation will be an ongoing process for many decades and centuries, requiring the support of climate services, which make the links between science, policy and adaptation practice

Climate change-driven coastal erosion modelling in temperate sandy beaches: Methods and uncertainty treatment

Developing future projections of shoreline change requires a good understanding of the driving coastal processes. These processes result primarily from the combination of mean sea level, waves, storm surges and tides, which are affected by global and regional climate change, and whose uncertainty increases with time. This paper reviews the current state of the art of methods used to model climate change-induced coastal erosion focusing on how climate change-related drivers and the associated uncertainty are considered. We identify research gaps, describe and analyse the key components of a comprehensive framework to derive future estimates of shoreline change and make suggestions for good practice. Within the scope of the review, we find that although significant progress has been made over the last decade, most of the studies limit uncertainty sampling to considering ranges of variation of forcing variables and ensembles of emissions scenarios, and applications with high level of probabilistic development remain few. Further research is necessary to fully (a) incorporate projected time series of coastal drivers into the erosion models, including bias correction; (b) sufficiently sample the uncertainty associated with each step of the top-down approach, including the consideration of different emission scenarios, inter- and intra-model variability, and multiple runs of erosion models or model ensembles; and (c) reduce uncertainty in shoreline change estimates by developing better datasets and model parameterisations, and progressing in detection and attribution