Water Governance in Bangladesh: An Evaluation of Institutional and Political Context

Water crises are often crises of governance. To address interrelated issues of securing access to sustainable sources of safe water for the world’s populations, scholar and practitioners have suggested fostering improved modes of water governance that support the implementation of integrated water resource management (IWRM). Recently, implementation of an IWRM approach was announced as a target for achieving Goal 6 of the Sustainable Development Goals (SDGs). This study employs an analytical hierarchy process with a SWOT analysis to assess the current institutional and political context of water governance in Bangladesh and evaluate IWRM as a means to achieve the SDGs

Discovery of Antagonist Peptides against Bacterial Helicase-Primase Interaction in B. stearothermophilus by Reverse Yeast Three-Hybrid

SummaryDeveloping small-molecule antagonists against protein-protein interactions will provide powerful tools for mechanistic/functional studies and the discovery of new antibacterials. We have developed a reverse yeast three-hybrid approach that allows high-throughput screening for antagonist peptides against essential protein-protein interactions. We have applied our methodology to the essential bacterial helicase-primase interaction in Bacillus stearothermophilus and isolated a unique antagonist peptide. This peptide binds to the primase, thus excluding the helicase and inhibiting an essential interaction in bacterial DNA replication. We provide proof of principle that our reverse yeast three-hybrid method is a powerful “one-step” screen tool for direct high-throughput antagonist peptide selection against any protein-protein interaction detectable by traditional yeast two-hybrid systems. Such peptides will provide useful “leads” for the development of new antibacterials

A facile approach to tryptophan derivatives for the total synthesis of argyrin analogues

A facile route has been established for the synthesis of indole-substituted (S)-tryptophans from corresponding indoles, which utilizes a chiral auxiliary-facilitated Strecker amino acid synthesis strategy. The chiral auxiliary reagents evaluated were (S)-methylbenzylamine and related derivatives. To illustrate the robustness of the method, eight optically pure (S)-tryptophan analogues were synthesized, which were subsequently used for the convergent synthesis of a potent antibacterial agent, argyrin A and its analogues

New found hope for antibiotic discovery: lipid II inhibitors

Research into antibacterial agents has recently gathered pace in light of the disturbing crisis of antimicrobial resistance. The development of modern tools offers the opportunity of reviving the fallen era of antibacterial discovery through uncovering novel lead compounds that target vital bacterial cell components, such as lipid II. This paper provides a summary of the role of lipid II as well as an overview and insight into the structural features of macrocyclic peptides that inhibit this bacterial cell wall component. The recent discovery of teixobactin, a new class of lipid II inhibitor has generated substantial research interests. As such, the significant progress that has been achieved towards its development as a promising antibacterial agent is discussed

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour

With the advancement in technology, telematics data which capture vehicle movements information are becoming available to more insurers. As these data capture the actual driving behaviour, they are expected to improve our understanding of driving risk and facilitate more accurate auto-insurance ratemaking. In this paper, we analyze an auto-insurance dataset with telematics data collected from a major European insurer. Through a detailed discussion of the telematics data structure and related data quality issues, we elaborate on practical challenges in processing and incorporating telematics information in loss modelling and ratemaking. Then, with an exploratory data analysis, we demonstrate the existence of heterogeneity in individual driving behaviour, even within the groups of policyholders with and without claims, which supports the study of telematics data. Our regression analysis reiterates the importance of telematics data in claims modelling; in particular, we propose a speed transition matrix that describes discretely recorded speed time series and produces statistically significant predictors for claim counts. We conclude that large speed transitions, together with higher maximum speed attained, nighttime driving and increased harsh braking, are associated with increased claim counts. Moreover, we empirically illustrate the learning effects in driving behaviour: we show that both severe harsh events detected at a high threshold and expected claim counts are not directly proportional with driving time or distance, but they increase at a decreasing rate

A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model

A well-designed framework for risk classification and ratemaking in automobile insurance is key to insurers' profitability and risk management, while also ensuring that policyholders are charged a fair premium according to their risk profile. In this paper, we propose to adapt a flexible regression model, called the Mixed LRMoE, to the problem of a posteriori risk classification and ratemaking, where policyholder-level random effects are incorporated to better infer their risk profile reflected by the claim history. We also develop a stochastic variational Expectation-Conditional-Maximization algorithm for estimating model parameters and inferring the posterior distribution of random effects, which is numerically efficient and scalable to large insurance portfolios. We then apply the Mixed LRMoE model to a real, multiyear automobile insurance dataset, where the proposed framework is shown to offer better fit to data and produce posterior premium which accurately reflects policyholders' claim history

Enhanced uptake of nanoparticle drug carriers via a thermoresponsive shell enhances cytotoxicity in a cancer cell line

Polymer particles consisting of a biodegradable poly[lactide-co-glycolide] (PLGA) core and a thermoresponsive shell have been formulated to encapsulate the dye rhodamine 6G and the potent cytotoxic drug paclitaxel. Cellular uptake of these particles is significantly enhanced above the thermal transition temperature (TTT) of the polymer shells in the human breast carcinoma cell line MCF-7 as determined by flow cytometry and fluorescence microscopy. Paclitaxel-loaded particles display reduced and enhanced cytotoxicity below and above the TTT respectively compared to unencapsulated drug. The data suggests a potential route to enhanced anti-cancer efficacy through temperature-mediated cell targeting.© The Royal Society of Chemistry 2013

Bis(μ-2-tert-butyl­phenyl­imido-1:2κ2 N:N)chlorido-2κCl-(diethyl ether-1κO)(2η5-penta­methyl­cyclo­penta­dien­yl)lithiumtantalum(V)

In the title compound, [LiTa(C10H15)(C10H13N)2Cl(C4H10O)], the TaV atom is coordinated by a η5-penta­methyl­cyclo­penta­dienyl (Cp*) ligand, a chloride ion and two N-bonded 2-tert-butyl­phenyl­imide dianions. With respect to the two N atoms, the chloride ion and the centroid of the Cp* ring, the tantalum coordination geometry is approximately tetra­hedral. The lithium cation is bonded to both the 2-tert-butyl­phenyl­imide dianions and also a diethyl ether mol­ecule, in an approximate trigonal planar arrangement. The Ta⋯Li separation is 2.681 (15) Å. In the crystal, a weak C—H⋯Cl inter­action links the mol­ecules. When compared to the 2,6-diisopropyl­phenyl­imide analogue (‘the Wigley derivative’) of the title compound, the two structures are conformationally matched with an overall r.m.s. difference of 0.461Å

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion