Conformation, energy, and folding ability of selected amino acid sequences.

Evolutionary selection of sequences is studied with a knowledge-based Hamiltonian to find the design principle for folding to a model protein structure. With sequences selected by naive energy minimization, the model structure tends to be unstable and the folding ability is low. Sequences with high folding ability have only the low-lying energy minimum but also an energy landscape which is similar to that found for the native sequence over a wide region of the conformation space. Though there is a large fluctuation in foldable sequences, the hydrophobicity pattern and the glycine locations are preserved among them. Implications of the design principle for the molecular mechanism of folding are discussed.

The Impact of Prefrontal Cortex "Warm Up" on Immediate Cognitive Reappraisal Ability in Older Adolescents with Elevated Symptoms of Depression

Cognitive Reappraisal (CR) is a central component of Cognitive Behavioral Therapy for adolescent depression. Yet, previous research indicates that a brain region highly associated with successful CR in adults, the Prefrontal Cortex (PFC), is not fully developed until early adulthood. Thus, there is growing concern that CBT interventions directed at building CR abilities in depressed teens might be constrained by PFC immaturity. However, CR is an effective strategy for regulating affect. The current study evaluated an intervention aimed at enhancing CR performance through PFC “warm up” with a working memory task. Additionally, the study examined moderators of intervention response, as well as cognitive correlates of self-reported CR use. Participants included 48 older adolescents (mean age=19.1, 89% female) with elevated symptoms of depression who were randomly assigned to a lab-based WM or control activity followed by a CR task. Overall, results failed to support the effectiveness of “warm up” to augment CR performance. However, current level of depression predicted negative bias and sadness ratings after CR instructions, and this effect was qualified by an interaction with condition. The moderator analysis showed that depressive symptoms interacted with condition such that in the control condition, participants with higher depressive symptoms had significantly lower negative bias scores than individuals with lower depressive symptoms, but this pattern was not found in the experimental condition. Contrary to hypotheses, history of depression did not moderate treatment response. Additional analyses explored alternative explanations for the lack of intervention effects. There was some evidence to suggest that the WM task was frustrating and cognitively taxing. However, irritation scores and overall WM task accuracy did not predict subsequent CR performance. Lastly, multiple cognitive variables emerged as correlates of self-reported CR use, with cognitive flexibility contributing unique variance to self-reported CR use. Results pointed to new directions for improving CR performance among youth with elevated symptoms of depression.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm

We present a derivative-free optimization algorithm coupled with a chemical process simulator for the optimal design of individual and complex distillation processes using a rigorous tray-by-tray model. The proposed approach serves as an alternative tool to the various models based on nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) . This is accomplished by combining the advantages of using a commercial process simulator (Aspen Hysys), including especially suited numerical methods developed for the convergence of distillation columns, with the benefits of the particle swarm optimization (PSO) metaheuristic algorithm, which does not require gradient information and has the ability to escape from local optima. Our method inherits the superstructure developed in Yeomans, H.; Grossmann, I. E.Optimal design of complex distillation columns using rigorous tray-by-tray disjunctive programming models. Ind. Eng. Chem. Res.2000, 39 (11), 4326–4335, in which the nonexisting trays are considered as simple bypasses of liquid and vapor flows. The implemented tool provides the optimal configuration of distillation column systems, which includes continuous and discrete variables, through the minimization of the total annual cost (TAC). The robustness and flexibility of the method is proven through the successful design and synthesis of three distillation systems of increasing complexity.