Structural basis of the chiral selectivity of Pseudomonas cepacia lipase

To investigate the enantioselectivity of Pseudomonas cepacia lipase, inhibition studies were performed with SC- and RC-(RP,SP)-1,2-dialkylcarbamoylglycero-3-O-p-nitrophenyl alkylphosphonates of different alkyl chain lengths. P. cepacia lipase was most rapidly inactivated by RC-(RP,SP)-1,2-dioctylcarbamoylglycero-3-O-p-nitrophenyl octylphosphonate (RC-trioctyl) with an inactivation half-time of 75 min, while that for the SC-(RP,SP)-1,2-dioctylcarbamoylglycero-3-O-p-nitrophenyl octyl-phosphonate (SC-trioctyl) compound was 530 min. X-ray structures were obtained of P. cepacia lipase after reaction with RC-trioctyl to 0.29-nm resolution at pH 4 and covalently modified with RC-(RP,SP)-1,2-dibutylcarbamoylglycero-3-O-p-nitrophenyl butyl-phosphonate (RC-tributyl) to 0.175-nm resolution at pH 8.5. The three-dimensional structures reveal that both triacylglycerol analogues had reacted with the active-site Ser87, forming a covalent complex. The bound phosphorus atom shows the same chirality (SP) in both complexes despite the use of a racemic (RP,SP) mixture at the phosphorus atom of the triacylglycerol analogues. In the structure of RC-tributyl-complexed P. cepacia lipase, the diacylglycerol moiety has been lost due to an aging reaction, and only the butyl phosphonate remains visible in the electron density. In the RC-trioctyl complex the complete inhibitor is clearly defined; it adopts a bent tuning fork conformation. Unambiguously, four binding pockets for the triacylglycerol could be detected: an oxyanion hole and three pockets which accommodate the sn-1, sn-2, and sn-3 fatty acid chains. Van der Waals’ interactions are the main forces that keep the radyl groups of the triacylglycerol analogue in position and, in addition, a hydrogen bond to the carbonyl oxygen of the sn-2 chain contributes to fixing the position of the inhibitor.

Anticancer therapy and apoptosis imaging

Early response prediction is considered an essential tool to obtain a more customized anticancer treatment because it allows for the identification of patients who will benefit most from a particular therapy and prevents the exposure of those patients to toxic, non-effective regimens. Recent discoveries of novel markers in functional imaging have created exciting opportunities for in vivo visualization and quantification of cell death. This review will focus on in vivo apoptosis imaging with various radiotracers as predictive tools for tumor response after anticancer therapy. Particular focus will be on annexin V imaging, a technique with the largest clinical experience to date. This article is part of a Special Issue entitled “Apoptosis: Four Decades Later”

Friendly Fire Off

The increasing popularity of online videogames has raised questions concerning their potential to influence online and offline social behaviour. Previous research on social behaviour in relation to playing videogames has often focused on either cooperation (playing in pairs against the game) or competition (playing alone against other players); however, videogames, particularly multiplayer online games, often include both. This study investigates prosocial behaviour in videogames with both cooperative and competitive elements—team-based player versus player (PvP) games—and aims to examine whether the amount of time spent playing these games is related to in-game prosocial behaviour. A cross-sectional survey was conducted among 727 respondents and results were analysed using conditional process modelling. No significant direct or indirect relationship between the amount of time spent playing team-based PvP games and in-game prosocial behaviour was found. However, an exploratory linear regression analysis revealed a significant, positive relationship between in-game and offline prosocial behaviour. Implications and recommendations for future research are discussed

Обернені сингулярно збурені задачі типу «конвекція-дифузія» для двозв’язних областей

Побудовано асимптотичне розвинення розв’язків сингулярно збурених крайових задач типу «конвекція-дифузія» з невідомим коефіцієнтом дифузії, який залежить від координат двозв’язної області. При побудові алгоритму використано перехід від вихідної постановки конвективно-дифузійної задачі у криволінійній двозв’язній області до періодичної задачі стосовно відповідної області комплексного потенціалу. Наведено результати числових розрахунків.The asymptotic expansion is constructed for solving singular disturbed boundary-value «convection-diffusion» problems with the unknown coefficient of diffusion which depends on physical coordinates of a double-connected area. Transition from the initial formulation of «convectional-diffusion» problems in curvilinear double-connected area to periodic task at the corresponding area complex potential is used to algorithm construction. The results of numerical calculations are given.Построено асимптотическое развитие решений сингулярно возмущенных краевых задач типа «конвекция-диффузия» с неизвестным коэффициентом диффузии, зависящем от координат двухсвязной области. При построении алгоритма использовано переход от исходной постановки конвективно-диффузионной задачи в криволинейной двусвязной области к периодической задаче относительно соответствующей области комплексного потенциала. Приведены результаты численных расчетов

Local-HDP:Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, the online variational inference method has been adapted for fast posterior approximation in the Local-HDP model. Experiments show that the proposed Local-HDP method outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency by a large margin. Moreover, two robotic experiments have been conducted to show the applicability of the proposed approach in real-time applications

Local-HDP:Interactive Open-Ended 3D Object Categorization

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin

Local-HDP:Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, the online variational inference method has been adapted for fast posterior approximation in the Local-HDP model. Experiments show that the proposed Local-HDP method outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency by a large margin. Moreover, two robotic experiments have been conducted to show the applicability of the proposed approach in real-time applications.Comment: 13 page

Local-HDP:Interactive Open-Ended 3D Object Categorization

We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin