Molecular interaction networks in the analyses of sequence variation and proteomics data

Protein–protein interaction networks are typically generated in standard cell lines or model organisms as it is prohibitively difficult to record large interaction datasets from specific tissues or disease models at a reasonable pace. Although the interaction data are of high confidence, they thus do not reflect in vivo relationships as such. A wealth of physiologically relevant protein information, obtained under different conditions and from different systems, is available including information on genetic variation, protein levels, and PTMs. However, these data are difficult to assess comprehensively because the relationships between the entities remain elusive from the measurements. Here, we exemplarily highlight recent studies that gained deeper insight from genetic variation, protein, and PTM measurements using interaction information pointing toward the importance and potential of interaction networks for the interpretation of sequencing and proteomics data.     

Adaptive detection of range-spread targets without secondary data in multichannel autoregressive process

Adaptive detection of range-spread targets without secondary data is addressed in a multichannel autoregressive Gaussian disturbance with unknown space–time covariance matrix, by utilizing the Rao test. The proposed Rao test without secondary data is theoretically proved to be asymptotically (large-sample in the number of temporal observations) constant false alarm rate with respect to unknown space–time covariance matrix, thanks to an asymptotic equivalence between the Rao test and the generalized likelihood ratio test. Moreover, the performance loss due to no secondary data can be remedied by appropriately increasing the temporal dimension. The performance assessment conducted by Monte Carlo simulation, also in comparison with the existing detector without secondary data, confirms the effectiveness of the proposed detectors.     

An impulse control approach to dike height optimization

This paper determines the optimal timing of dike heightenings as well as the corresponding optimal dike heightenings to protect against floods. To derive the optimal policy, we design an algorithm based on the Impulse Control Maximum Principle. In this way, this paper presents one of the first real-life applications of the Impulse Control Maximum Principle developed by Blaquière. We show that the proposed impulse control (IC) approach performs better than dynamic programming with respect to computational time. This is caused by the fact that IC does not need discretization in time.     

Particle filters and occlusion handling for rigid 2D–3D pose tracking

In this paper, we address the problem of 2D–3D pose estimation. Specifically, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose (position and orientation) in 3D space. We revisit a joint 2D segmentation/3D pose estimation technique, and then extend the framework by incorporating a particle filter to robustly track the object in a challenging environment, and by developing an occlusion detection and handling scheme to continuously track the object in the presence of occlusions. In particular, we focus on partial occlusions that prevent the tracker from extracting an exact region properties of the object, which plays a pivotal role for region-based tracking methods in maintaining the track. To this end, a dynamical choice of how to invoke the objective functional is performed online based on the degree of dependencies between predictions and measurements of the system in accordance with the degree of occlusion and the variation of the object’s pose. This scheme provides the robustness to deal with occlusions of an obstacle with different statistical properties from that of the object of interest. Experimental results demonstrate the practical applicability and robustness of the proposed method in several challenging scenarios.     

An efficient and secure approach for a cloud collaborative editing

The demand for cloud-based collaborative editing service is rising along with the tremendously increased popularity in cloud computing. In the cloud-based collaborative editing environment, the data are stored in the cloud and able to be accessed from everywhere through every compatible device with the Internet. The information is shared with every accredited user in a group. In other words, multiple authorized users of the group are able to work on the same document and edit the document collaboratively and synchronously online. Meanwhile, during the whole collaborative editing process, the encryption technique is eventually applied to protect and secure the data. The encryption for the collaborative editing, however, could require much time to operate. To elevate the efficiency of the encryption, this study first analyzes the text editing in the collaborative service and presents a framework of the Red–Black tree, named as rbTree-Doc. The rbTree-Doc can reduce the amount of data to be encrypted. Although the trade-off for creating the Red–Black tree introduces extra cost, the experimental results of using rbTree-Doc in text editing operations, such as insertion and removal, show improved efficiency compared with other whole-document encryption strategy. Using rbTree-Doc, the efficiency is improved by 31.04% compared to that 3DES encryption is applied and by 23.94% compared to that AES encryption is applied.     

Introduction to the 100th anniversary issue of the Psychological Review..

Discusses the evolution of the Psychological Review from its inception in 1894 as a general journal to its current status as a forum for theoretical discussion. A historical overview of Psychological Review is presented, and its 2 main subfields, cognition and perception, are outlined. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Trends in psychotherapy process research: Samples, measures, researchers, and classic publications.

Psychotherapy studies published in the Journal of Counseling Psychology (JCP) and the Journal of Consulting and Clinical Psychology (JCCP) between 1978 and 1992 were examined. Differences were found between the 2 journals. JCP published mostly process, outcome, and analog research, whereas JCCP published mostly outcome research. Most process and process-outcome studies across journals were of individual, brief therapy. Across the years, more diversity was evident in samples used in process research in terms of student status, gender, and race of clients and therapists. Three content areas were prominent in the process measures and classic studies: therapist techniques, therapist influence, and facilitative conditions. Lists are provided of the frequently used measures, most productive authors, and classic studies in process research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parameter identification and position control for helical hydraulic rotary actuators based on particle swarm optimization

It is difficult to obtain an accurate mathematical model in electro-hydraulic servo control system, due to the nonlinear factors such as dead zone, saturation, flow coefficient, and friction. Hence, a parameter identification algorithm, combining recursive least squares (RLS) with modified nonlinear particle swarm optimization (NPSO) algorithm, is proposed. On this basis, another improved NPSO algorithm is also put forward, aiming at searching for the optimal proportional–integral (PI) controller gain of the nonlinear hydraulic system while giving comprehensive consideration to the system performance indexes. The system identification experiments and position tracking control are conducted, respectively. As indicated by the comparison with the least squares (LS), RLS, PSO, and RLS–LPSO results, the proposed method shows higher identification and control accuracy.     

Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.     

Surface Control and Electrical Tuning of MXene Electrode for Flexible Self-Powered Human–Machine Interaction

MXene materials emerge as promising candidates for energy harvesting and storage application. In this study, the effect of the surface chemistry on the work function of MXenes, which determines the performance of MXene-based triboelectric nanogenerator (TENG), is elucidated. First-principles calculations reveal that the surface functional group greatly influences MXene work function:  OH termination reduces the work function with respect to that of bare surface, while  F and  Cl increase it. Then, work functions are experimentally determined by Kelvin probe force microscopy. The MXene prepared by gentle etching at 40 °C for 48 h (GE_40/48) has the largest work function. Furthermore, an electron-cloud potential-well model is established to explain the mechanism of electron emission-dominated charge transfer and assemble a triboelectric device to verify experimentally its conclusions. It is found that GE_40/48 has the best performance with a 281 V open-circuit voltage, 9.7 µA short-current current, and storing 1.019 µC of charge, which is consistent with the model. Last, a patterned TENG is demonstrated for self-powered human–machine interaction application. This finding enhances the understanding of the inherent mechanism between the surface structure and the output performance of MXene-based TENG, which can be applied to other TENG based on 2D materials.