Design of an Ultrafast G Protein Switch Based on a Mouse Melanopsin Variant

The primary goal of optogenetics is the light-controlled noninvasive and specific manipulation of various cellular processes. Herein, we present a hybrid strategy for targeted protein engineering combining computational techniques with electrophysiological and UV/visible spectroscopic experiments. We validated our concept for channelrhodopsin-2 and applied it to modify the less-well-studied vertebrate opsin melanopsin. Melanopsin is a promising optogenetic tool that functions as a selective molecular light switch for G protein-coupled receptor pathways. Thus, we constructed a model of the melanopsin G_q protein complex and predicted an absorption maximum shift of the Y211F variant. This variant displays a narrow blue-shifted action spectrum and twofold faster deactivation kinetics compared to wild-type melanopsin on G protein-coupled inward rectifying K⁺ (GIRK) channels in HEK293 cells. Furthermore, we verified the in vivo activity and optogenetic potential for the variant in mice. Thus, we propose that our developed concept will be generally applicable to designing optogenetic tools.     

Incorporating Intra‐Query Term Dependencies in an Aspect Query Language Model

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra‐query term dependencies (i.e., the dependencies between different query terms and term combinations) have not yet been sufficiently addressed in the existing approaches. This article aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model (AM). We propose to extend the AM with a hidden Markov model (HMM) structure to incorporate the intra‐query term dependencies and learn the structure of a novel aspect HMM (AHMM) for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an ergodic HMM, where the dependencies between latent variables (nodes) are modeled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large‐scale text retrieval conference (TREC) collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness but also achieves better results than the AM and another state‐of‐the‐art approach, namely the latent concept expansion model. © 2014 Wiley Periodicals, Inc.     

Flow shop batching and scheduling with sequence-dependent setup times

This paper addresses the flow shop batching and scheduling problem where sequence-dependent family setup times are present and the objective is to minimize makespan. We consider violating the group technology assumption by dividing product families into batches. In order to reduce setup times, inconsistent batches are formed on different machines, which lead to non-permutation schedules. To the best of our knowledge, this is the first time that the splitting of job families into inconsistent batches has been considered in a flow shop system. A tabu search algorithm is developed which contains several neighbourhood functions, double tabu lists and a multilevel diversification structure. Compared to the state-of-the-art meta-heuristics for this problem, the proposed tabu search algorithm achieves further improvement when the group scheduling assumption is dropped. Also, various experiments conducted on the benchmark problem instances confirm the benefits of batching. Therefore, it will be prudent for the practitioners to consider adopting inconsistent batches and non-permutation schedules to improve their operational efficiency within a reasonable amount of computational effort.     

Optimal allocation of change points in simple step-stress experiments under Type-II censoring

In simple step-stress experiments under Type-II censoring with the cumulative exposure model and exponentially distributed lifetimes, maximum likelihood estimates (MLE) of the expected lifetimes may not exist due to the absence of failure times either before or after the stress change point. For this reason, when planning a step-stress experiment, the change point could be chosen so as to minimize the probability of non-existence of the MLE. These non-existence probabilities are examined and compared in the one- as well as the two-sample situations. Moreover, the optimal allocations of the change points are discussed and the effects of the use of non-optimal choices for the change points are assessed.     

Efficient computation based on stochastic spikes

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.     

Particle Monte Carlo and lattice-Boltzmann methods for simulations of gas-particle flows

A numerical solution concept is presented for simulating the transport and deposition to surfaces of discrete, small (nano-)particles. The motion of single particles is calculated from the Langevin equation by Lagrangian integration under consideration of different forces such as drag force, van der Waals forces, electrical Coulomb forces and not negligible for small particles, under stochastic diffusion (Brownian diffusion). This so-called particle Monte Carlo method enables the computation of macroscopic filter properties as well the detailed resolution of the structure of the deposited particles. The flow force and the external forces depend on solutions of continuum equations, as the Navier-Stokes equations for viscous, incompressible flows or a Laplace equation of the electrical potential. Solutions of the flow and potential fields are computed here using lattice-Boltzmann methods. Essential advantage of these methods are the easy and efficient treatment of three-dimensional complex geometries, given by filter geometries or particle covered surfaces. A number of numerical improvements, as grid refinement or boundary fitting, were developed for lattice-Boltzmann methods in previous studies and applied to the present problem. The interaction between the deposited particle layer and the fluid field or the external forces is included by recomputing of these fields with changed boundaries. A number of simulation results show the influence of different effects on the particle motion and deposition.     

Die Restspannungen in der Umgebung der kaltverfestigten Bohrung

Zusammenfassung Gegenstand der Untersuchung ist die Spannungsverteilung in der unendlichen elastisch-plastischen Scheibe, die durch die Druckbelastung einer kreisf?rmigen Bohrung hervorgerufen wird. Der Rechnung liegt die Trescasche Flie?bedingung, die zugeordnete Flie?regel sowie lineare isotrope Verfestigung zugrunde. Bestimmt werden ferner die Restspannungen und die Aufweitung der Bohrung.