Concurrent Deficits in Behavior Inhibition, Non-verbal Working Memory and Psychological Sense of Time in ADHD

According to the Hybrid Model of Executive Function for Attention Deficit Hyperactivity Disorder (ADHD), hyperactive and combined types, a delay in behavior inhibition causes secondary deficits in four executive function; non-verbal working memory, verbal working memory, reconstitution and self-regulation of affect/motivation/arousal. The deficit in non-verbal working memory causes a deficit in psychological sense of time, which in tum impairs self-regulation in those with ADHD. This single case study investigated concurrent deficits in behavior inhibition, non-verbal working memory and psychological sense of time in a 1O-year-old male with ADHD, combined type. Three interrelated components of behavior inhibition were measured by the Continuous Performance Test-II, The Wisconsin Card Sorting Test, and the Stroop Test. Non-verbal working memory was measured by using the Rey-Complex Figure Test and Recognition Trial, and the psychological sense of time was measured by the Time Perception Test, which is a time reproduction task. The results of this case study supports the Hybrid Model of Executive Function as concurrent deficits in behavior inhibition, non-verbal working memory and psychological sense of time were found in a subject with ADHD, combined type. The implications of these findings for treatment and future research are discussed

The effects of multiple aerospace environmental stressors on human performance

An extended Fitt's law paradigm reaction time (RT) task was used to evaluate the effects of acceleration on human performance in the Dynamic Environment Simulator (DES) at Armstrong Laboratory, Wright-Patterson AFB, Ohio. This effort was combined with an evaluation of the standard CSU-13 P anti-gravity suit versus three configurations of a 'retrograde inflation anti-G suit'. Results indicated that RT and error rates increased 17 percent and 14 percent respectively from baseline to the end of the simulated aerial combat maneuver and that the most common error was pressing too few buttons

Enhancing electrochemical intermediate solvation through electrolyte anion selection to increase nonaqueous Li-O2_2 battery capacity

Among the 'beyond Li-ion' battery chemistries, nonaqueous Li-O$_2$ batteries have the highest theoretical specific energy and as a result have attracted significant research attention over the past decade. A critical scientific challenge facing nonaqueous Li-O$_2$ batteries is the electronically insulating nature of the primary discharge product, lithium peroxide, which passivates the battery cathode as it is formed, leading to low ultimate cell capacities. Recently, strategies to enhance solubility to circumvent this issue have been reported, but rely upon electrolyte formulations that further decrease the overall electrochemical stability of the system, thereby deleteriously affecting battery rechargeability. In this study, we report that a significant enhancement (greater than four-fold) in Li-O$_2$ cell capacity is possible by appropriately selecting the salt anion in the electrolyte solution. Using $^7$Li nuclear magnetic resonance and modeling, we confirm that this improvement is a result of enhanced Li$^+$ stability in solution, which in turn induces solubility of the intermediate to Li$_2$O$_2$ formation. Using this strategy, the challenging task of identifying an electrolyte solvent that possesses the anti-correlated properties of high intermediate solubility and solvent stability is alleviated, potentially providing a pathway to develop an electrolyte that affords both high capacity and rechargeability. We believe the model and strategy presented here will be generally useful to enhance Coulombic efficiency in many electrochemical systems (e.g. Li-S batteries) where improving intermediate stability in solution could induce desired mechanisms of product formation.Comment: 22 pages, 5 figures and Supporting Informatio

Leaf-inspired microcontact printing vascular patterns.

The vascularization of tissue grafts is critical for maintaining viability of the cells within a transplanted graft. A number of strategies are currently being investigated including very promising microfluidics systems. Here, we explored the potential for generating a vasculature-patterned endothelial cells that could be integrated into distinct layers between sheets of primary cells. Bioinspired from the leaf veins, we generated a reverse mold with a fractal vascular-branching pattern that models the unique spatial arrangement over multiple length scales that precisely mimic branching vasculature. By coating the reverse mold with 50 μg ml-1 of fibronectin and stamping enabled selective adhesion of the human umbilical vein endothelial cells (HUVECs) to the patterned adhesive matrix, we show that a vascular-branching pattern can be transferred by microcontact printing. Moreover, this pattern can be maintained and transferred to a 3D hydrogel matrix and remains stable for up to 4 d. After 4 d, HUVECs can be observed migrating and sprouting into Matrigel. These printed vascular branching patterns, especially after transfer to 3D hydrogels, provide a viable alternative strategy to the prevascularization of complex tissues

Important Considerations in Plasmon-Enhanced Electrochemical Conversion at Voltage-Biased Electrodes.

In this perspective we compare plasmon-enhanced electrochemical conversion (PEEC) with photoelectrochemistry (PEC). PEEC is the oxidation or reduction of a reactant at the illuminated surface of a plasmonic metal (or other conductive material) while a potential bias is applied. PEC uses solar light to generate photoexcited electron-hole pairs to drive an electrochemical reaction at a biased or unbiased semiconductor photoelectrode. The mechanism of photoexcitation of charge carriers is different between PEEC and PEC. Here we explore how this difference affects the response of PEEC and PEC systems to changes in light, temperature, and surface morphology of the photoelectrode

Memory Aware Synapses: Learning what (not) to forget

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201