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Storage Stability and Solution Binding Affinity of an Fc-Fusion Mimetic
Get PDFThis study evaluates the storage stability and solution binding affinity of a novel Fc-fusion mimetic, receptor-PEG-receptor (RpR), designed to address limitations of the current therapeutic aflibercept, a gold-standard therapy for age-macular degeneration (AMD). Using di(bis-sulfone) PEG linker as a structural scaffold, the mimetic aims to improve the storage stability and binding efficacy of the Fc fusion protein. Mass photometry and size-exclusion chromatography demonstrated that RpR, even in an unformulated buffer, exhibits superior storage stability exceeding 10 months compared to aflibercept. Furthermore, microscale thermophoresis was employed to determine RpR's binding affinity to VEGF in solution, providing a more physiologically relevant assessment than traditional binding assays. These findings highlight RpR's potential as a therapeutic candidate for the treatment of AMD disease, warranting further investigation
Creating Deeper Attachments: Reflections on Developing Arts-based Pedagogy and Practice within Psychology.
Get PDFIn this article we explore the potential of arts-based methodologies to contribute to pedagogy and practice in psychology. Drawing on insights developed by Elliot Eisner (2008) related to what education can learn from the arts, we first explore how Eisner’s ideas, along with our experiences, might infuse research supervision. Through ‘applied practice’ we then identify some of the potential benefits and challenges of including arts-based methods during supervision. Reflections show how important it is to create a safe space, deconstruct hierarchies, and provide examples of arts-based research, along with an opportunity to experiment and share. Together, these seem to support the development of trusting mature relationships that can lead to personal growth and transformation. While we have become advocates for the potential of arts-based research in psychology our reflections also identify a number of challenges and conditions to realising such benefits
Evaluation of Battery Management Systems for Electric Vehicles Using Traditional and Modern Estimation Methods.
Get PDFThis paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB’s Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability
Insights into substrate recognition and export tunnel preferences in the efflux transporter AcrB
No full textIn Escherichia coli AcrB is a major multidrug exporter, which confers the bacterium resistance to many antibiotics with divers structural and chemical proprieties. Studies have identified three possible tunnels (or channels) within AcrB that different substrates use before reaching the distal pocket, from which they are subsequently extruded. Recently, we reported that mutations in the AcrB gate loop may affect the conformational change kinetics involved in substrate export rather than directly affecting molecular interactions with this loop, and we highlighted the distinct export tunnel preferences between erythromycin and doxorubicin. To further understand the gate loop's role in AcrB's export activity and the rationale behind substrate preferences among the three possible export tunnels, namely, tunnel 1, 2, and 3, we investigated the structural and functional effects of several single and multiple mutations in the gate loop of AcrB. Our findings indicate that all three tunnels are energetically favourable for the substrates studied, with the majority forming more hydrogen bonds in any tunnel compared to the distal pocket. Moreover, our experimental and computational data revealed that some substrates with high molecular similarity exhibited different export tunnel preferences, as strongly suggested by their MIC values. To explain this unexpected outcome, we propose a generalised explanation that the conformational change kinetics in AcrB is substrate dependent
Hidden screen industries: the ‘Known unknown’ in screen history.
Get PDFThis is an introduction to the Hidden Screen Industries special dossier
Vulnerability and multilingualism in intercultural research with migrants: developing an inclusive research practice
Get PDFThis Special Issue explores the interplay of vulnerability and multilingualism in research with migrant and displaced communities, with a focus on methodological (including ethical) complexities and the development of ‘best practice’.
Increased mobility, migration and the recent conflicts in Europe and the Middle East have drawn attention to the vulnerabilities associated with migration, but how such vulnerabilities intersect with linguistic diversity is not so often attended to. As Blommaert et al. (Citation2005) have shown, migration and mobility can result in the devaluing of linguistic repertoires which had status in one location but are ascribed less or no value in the new context, giving rise to power asymmetries of various kinds including socioeconomic and epistemic inequalities associated with language. Such dynamics often lead to social injustices and vulnerabilities experienced by migrant and displaced communities, who for example may find themselves excluded from or marginalised by the mainstream education system. As researchers in intercultural communication, we need to be able to meet these linguistic, cultural and social demands, including openly and critically discussing the concepts of inclusive research, and negotiating vulnerability in research, in depth (Blommaert & Backus, Citation2013; Georgiou, Citation2022; Pinter, Citation2014; van Liempt & Bilger, Citation2012). Therefore, we consider the theme of negotiating vulnerability in research where multiple languages are involved to be of particular importance for our field and related fields
Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin
Get PDFPredicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting
A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification
Get PDFAccurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today’s more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects
A life cycle assessmen of building demolition waste: a comparison study
Get PDFGlobally, building demolition waste constitutes a considerable environmental problem. The environmental implications are not only associated with volume, but also with carbon embodied in the waste. These adverse environmental impacts associated with the generated waste can be minimised through appropriate waste management strategies. This study proposed a mathematical model from the end-of-life perspective to examine two waste treatment methods. The model was illustrated by a case study of three approved building construction systems by a current UK supermarket referred to as construction methods (CM1), (CM2) and (CM3) to assess the construction system with the least carbon emission. Landfilling and recycling were assumed as waste treatment methods to examine the preferable waste treatment method. Results showed that recycling is the most preferred method of waste treatment method of the supermarket. This was revealed by the amount of demolition waste material recycled (more than 90%) from each of the CM compared to the volume of waste materials landfilled (less than 10%) and the associated carbon emissions. Steel has the highest carbon reduction potential contributing nearly 80% in each case study compared to concrete about 1%. Finally, CM1 has the lowest carbon emission, with both CM2 and CM3 emitting approximately 3% more
Age and sex-specific disability-free life expectancy in urban and rural settings of Bangladesh
Get PDFBackground
Disability-free life expectancy (DFLE) has been used to gain a better understanding of the population’s quality of life.
Objectives
The authors aimed to estimate age and sex-specific disability-free life expectancy (DFLE) for urban and rural areas of Bangladesh, as well as to investigate the differences in DFLE between males and females of urban and rural areas.
Methods
Data from the Bangladesh Sample Vital Statistics-2016 and the Bangladesh Household Income and Expenditure Survey (HIES)-2016 were used to calculate the disability-free life expectancy (DFLE) of urban and rural males and females in Bangladesh in 2016. The DFLE was calculated using the Sullivan method.
Results
With only a few exceptions, rural areas have higher mortality and disability rates than urban areas. For both males and females, statistically significant differences in DFLE were reported between urban and rural areas between the ages of birth and 39 years. In comparison to rural males and females, urban males and females had a longer life expectancy (LE), a longer disability-free life expectancy, and a higher share of life without disability.
Conclusion
This study illuminates stark urban–rural disparities in LE and DFLE, especially among individuals aged < 1–39 years. Gender dynamics reveal longer life expectancy but shorter disability-free life expectancy for Bangladeshi women compared to men, emphasizing the need for targeted interventions to address these pronounced health inequalities