Mechanisms underlying neurocognitive dysfunctions in recurrent major depression

Recent work shows that depression is intimately associated with changes in cognitive functioning, including memory, attention, verbal fluency, and other aspects of higher-order cognitive processing. Changes in cognitive functioning are more likely to occur when depressive episodes are recurrent and to abate to some degree during periods of remission. However, with accumulating frequency and duration of depressive episodes, cognitive deficits can become enduring, being evident even when mood improves. Such changes in cognitive functioning give depression links to mild cognitive impairment and thereby with neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, schizophrenia, and multiple sclerosis. Depression may then be conceptualized on a dimension of depression - mild cognitive impairment - dementia. The biological underpinnings of depression have substantial overlaps with those of neurodegenerative conditions, including reduced neurogenesis, increased apoptosis, reactive oxygen species, tryptophan catabolites, autoimmunity, and immune-inflammatory processes, as well as decreased antioxidant defenses. These evolving changes over the course of depressive episodes drive the association of depression with neurodegenerative conditions. As such, the changes in cognitive functioning in depression have important consequences for the treatment of depression and in reconceptualizing the role of depression in wider neuroprogressive conditions. Here we review the data on changes in cognitive functioning in recurrent major depression and their association with other central conditions.

Challenge demands, hindrance demands, and psychological need satisfaction: their influence on employee engagement and emotional exhaustion

The job demands-resources (JD-R) model provides a well-validated account of how job resources and job demands influence work engagement, burnout, and their constituent dimensions. The present study aimed to extend previous research by including challenge demands not widely examined in the context of the JD-R. Furthermore, and extending self-determination theory, the research also aimed to investigate the potential mediating effects that employees' need satisfaction as regards their need for autonomy, need for belongingness, need for competence, and need for achievement, as components of a higher order needs construct, may have on the relationships between job demands and engagement. Structural equations modeling across two independent samples generally supported the proposed relationships. Further research opportunities, practical implications, and study limitations are discussed.

Self-assembled peptide nanostructures for the fabrication of cell scaffolds

The fabrication of artificial scaffolds that effectively mimic the host environment of the cell have exciting potential for the treatment of many diseases in regenerative medicine. In particular, appropriately designed scaffolds have the capacity to support, replace, and mediate the transplantation of therapeutic cells in order to regenerate damaged or diseased tissues. To achieve these goals for regeneration, the engineering of an environment structurally similar to the native extracellular matrix (ECM) is necessary in order to closely match the chemical and physical conditions found within the extracellular niche. Recently, self-assembled peptide (SAP) hydrogels have shown great potential for such biological applications due to their inherent biocompatibility, propensity to form higher order structures, rich chemical functionality and ease of synthesis. Importantly, it is possible to control the organization and properties of the target materials as the chemical structure is determined by amino acid sequence. Here, the development of SAP hydrogels as functional cell scaffolds and useful tools in tissue engineering is reviewed.

Tensor-variate restricted Boltzmann machines

Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm

The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator's physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver's head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver's head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.