Design and Validation of a Bifunctional Ligand Display System for Receptor Targeting

AbstractHere we developed a bacteriophage display particle designed to serve as a bifunctional entity that can target tumors while delivering an agent. We engineered a chimera phage vector containing a pIII-displayed αv integrins-targeting moiety and a pVIII-displayed streptavidin binding adaptor moiety. By using the chimeric phage particle, targeting of αv integrins on cells in culture and tumor-related blood vessels was shown through different applications, including luminescent quantum dots localization, surface plasmon resonance-based binding detection, and an in vivo tumor model. The strategy validated here will accelerate the discovery and characterization of receptor-ligand binding events in high throughput, and cell-specific delivery of diagnostics or therapeutics to organs of choice without the need for chemical conjugation

The Λ\Lambda-Σ\Sigma coupling effect in the neutron-rich Λ\Lambda-hypernucleus Λ10_{\Lambda}^{10}Li by microscopic shell model

We investigate the structure of the neutron-rich $\Lambda$-hypernucleus $_{\Lambda}^{10}$Li by using microscopic shell-model calculations considering a $\Lambda$-$\Sigma$ coupling effect. The calculated $\Sigma$-mixing probability in the $_{\Lambda}^{10}$Li ground state is found to be about 0.34 % which is coherently enhanced by the $\Lambda$-$\Sigma$ coupling configurations, leading to the energy shift 0.28 MeV which is about 3 times larger than that in $_{\Lambda}^{7}$Li. The importance of the $\Sigma$ configuration obtained by the $\Sigma N$ interaction and the potentiality of the neutron-rich environment are discussed.Comment: 6 figure

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments

Anti-malarial landscape in Myanmar: results from a nationally representative survey among community health workers and the private sector outlets in 2015/2016

Abstract Background In 2015/2016, an ACTwatch outlet survey was implemented to assess the anti-malarial and malaria testing landscape in Myanmar across four domains (Eastern, Central, Coastal, Western regions). Indicators provide an important benchmark to guide Myanmar’s new National Strategic Plan to eliminate malaria by 2030. Methods This was a cross-sectional survey, which employed stratified cluster-random sampling across four regions in Myanmar. A census of community health workers (CHWs) and private outlets with potential to distribute malaria testing and/or treatment was conducted. An audit was completed for all anti-malarials, malaria rapid diagnostic tests. Results A total of 28,664 outlets were approached and 4416 met the screening criteria. The anti-malarial market composition comprised CHWs (41.5%), general retailers (27.9%), itinerant drug vendors (11.8%), pharmacies (10.9%), and private for-profit facilities (7.9%). Availability of different anti-malarials and diagnostic testing among anti-malarial-stocking CHWs was as follows: artemisinin-based combination therapy (ACT) (81.3%), chloroquine (67.0%), confirmatory malaria test (77.7%). Less than half of the anti-malarial-stocking private sector had first-line treatment in stock: ACT (41.7%) chloroquine (41.8%), and malaria diagnostic testing was rare (15.4%). Oral artemisinin monotherapy (AMT) was available in 27.7% of private sector outlets (Western, 54.1%; Central, 31.4%; Eastern; 25.0%, Coastal; 15.4%). The private-sector anti-malarial market share comprised ACT (44.0%), chloroquine (26.6%), and oral AMT (19.6%). Among CHW the market share was ACT (71.6%), chloroquine (22.3%); oral AMT (3.8%). More than half of CHWs could correctly state the national first-line treatment for uncomplicated falciparum and vivax malaria (59.2 and 56.9%, respectively) compared to the private sector (15.8 and 13.2%, respectively). Indicators on support and engagement were as follows for CHWs: reportedly received training on malaria diagnosis (60.7%) or national malaria treatment guidelines (59.6%), received a supervisory or regulatory visit within 12 months (39.1%), kept records on number of patients tested or treated for malaria (77.3%). These indicators were less than 20% across the private sector. Conclusion CHWs have a strong foundation for achieving malaria goals and their scale-up is merited, however gaps in malaria commodities and supplies must be addressed. Intensified private sector strategies are urgently needed and must be scaled up to improve access and coverage of first-line treatments and malaria diagnosis, and remove oral AMT from the market place. Future policies and interventions on malaria control and elimination in Myanmar should take these findings into consideration across all phases of implementation

Mean-VaR portfolio optimization: A nonparametric approach

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice

Assay Type Detection Using Advanced Machine Learning Algorithms

The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.</p