3D multi-agent models for protein release from PLGA spherical particles with complex inner morphologies

In order to better understand and predict the release of proteins from bioerodible micro- or nanospheres, it is important to know the influences of different initial factors on the release mechanisms. Often though it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte-Carlo (MC) and Cellular Automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins. Results have confirmed that this modelling approach has increased the resolution over the complexity involved, opening promising perspectives for future developments, especially complementing in vitro experimentation

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

A journey into Almere’s diverse diet

There is a strong relation between food and identity. Especially when people move to another country, traditional food (or simply food from their country of origin), symbolizes a link with culture, communityand ethnic identity. As people move around the globe they introduce new foods in the places they land. Almere is becoming one of the largest majority minority cities of the Netherlands. Walking around thecity, the diversity of food ingredients and eating cultures as shown in shops and restaurants is immediately clear. The aim of this project was to get an insight into the diets of the residents of Almere so as to learn about eating patterns in a multicultural city and how multiculturality affects the diets of both newcomers and people who have been living here for generations

COVID-19 highlights the model dilemma in biomedical research

Scientists worldwide struggle to identify suitable animal models to study SARS-CoV-2 infections. Interspecies-related differences, such as host specificity, divergent immune responses, or the unavailability of species-specific reagents hamper the research. Human-based models, such as micro-engineered multi-organs-on-chip, may hold the solution

Bivariate Extension of the Quadrature Method of Moments for Batch Crystallization Models

This Article presents a bivariate extension of the quadrature method of moments for solving two-dimensional batch crystallization models involving crystals nucleation, size-dependent growths, aggregation, and dissolution of small nuclei below certain critical size in a dissolution unit. In this technique, orthogonal polynomials of lower order moments are used to find the quadrature abscissas (points) and weights. Several benchmark problems with different combinations of processes are considered in this Article. The accuracy and efficiency of the proposed method are validated against the analytical solutions and the high-resolution finite volume scheme. Excellent agreements were observed in all test problems. It was found that the current method is very efficient and accurate as compared to the high-resolution finite volume scheme. copyright 2010 American Chemical Society [accessed November 18th, 2010

Corporate governance, political connections, and bank performance

This study investigates the impact of corporate governance characteristics and political connections of directors on the profitability of banks in Pakistan. The study uses the data of 26 domestic banks over the latest and large period of 2007–2016. Our findings firstly affirm that bank profitability is negatively affected by the presence of politically connected directors on the board, reporting significantly lower return on assets, return on equity, net interest margin, and profit margin. Secondly, our findings also affirm the negative political influence on the sustainability of the banking industry, reporting significantly lower return on assets, return on equity, net interest margin, and profit margin during the government transition of banks having politically connected directors sitting on their board. Our findings further report an inverted U-shaped relationship between board size and bank profitability, suggesting that a board size beyond 8–9 members decreases the profitability. The study further finds a positive impact of board composition, board independence, and director compensation on bank profitability, while also finding a negative impact of frequent board meetings, presence of foreign directors, and audit committee independence

A Performance Study on the Signal-On-Fail Approach to Imposing Total Order in the Streets of Byzantium

Any asynchronous total-order protocol must somehow circumvent the well-known FLP impossibility result. This paper exposes the performance gains obtained when this impossibility is dealt with through the use of abstract processes built to have some special failure semantics. Specifically, we build processes with signal-on-fail semantics by (i) having a subset of Byzantine-prone processes paired to check each other’s computational outputs, and (ii) assuming that paired processes do not fail simultaneously. By dynamically invoking the construction of signal-on-fail processes, coordinator-based total-order protocols which allow less than one-third of processes to fail in a Byzantine manner are developed. Using a LAN-based implementation, failure-free order latencies and fail-over latencies are measured; the former are shown to be smaller compared to the protocol of Castro and Liskov which is generally regarded to perform exceedingly well in the best-case scenarios

A multi-tree genetic programming representation for melanoma detection using local and global features

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer

Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection

© 2019 IEEE. The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been utilised to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, color variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve better GP individuals having multiple trees influencing each other's performance during the evolution, in order to get overall performance gains. The performance of the new method is checked on two benchmark skin image datasets, and compared with six widely used classification algorithms and the single tree GP method. The experimental results have shown that the proposed method has significantly outperformed all these classification methods

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be particularly useful when exploring the selectivity and promiscuity of ligands on different proteins. In this review, we will firstly provide a brief introduction to the main concepts of PCM for readers new to the field. The next part focuses on recent technical advances, including the application of support vector machines (SVMs) using different kernel functions, random forests, Gaussian processes and collaborative filtering. The subsequent section will then describe some novel practical applications of PCM in the medicinal chemistry field, including studies on GPCRs, kinases, viral proteins (e.g.from HIV) and epigenetic targets such as histone deacetylases. Finally, we will conclude by summarizing novel developments in PCM, which we expect to gain further importance in the future. These developments include adding three-dimensional protein target information, application of PCM to the prediction of binding energies, and application of the concept in the fields of pharmacogenomics and toxicogenomics. This review is an update to a related publication in 2011 and it mainly focuses on developments in the field since then