Evidence for Glass–glass Interfaces in a Columnar Cu–Zr Nanoglass

Comprehensive analyses of the atomic structure using advanced analytical transmission electron microscopy-based methods combined with atom probe tomography confirm the presence of distinct glass–glass interfaces in a columnar Cu-Zr nanoglass synthesized by magnetron sputtering. These analyses provide first-time in-depth characterization of sputtered film nanoglasses and indicate that glass–glass interfaces indeed present an amorphous phase with reduced mass density as compared to the neighboring amorphous regions. Moreover, dedicated analyses of the diffusion kinetics by time-of-flight secondary ion mass spectroscopy (ToF SIMS) prove significantly enhanced diffusivity, suggesting fast transport along the low density glass–glass interfaces. The present results further indicate that sputter deposition is a feasible technique for reliable production of nanoglasses and that some of the concepts proposed for this new class of glassy materials are applicable.     

Edge detection and characterization of digitized images

Pattern Analysis and Applications - We present a method for determining which of a large set of pixels are inside or on the boundary of a polygon. The method works much quicker than the standard...     

Nano functional neural interfaces

Engineered functional neural interfaces (fNIs) serve as essential abiotic–biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms. fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.     

Closed-loop drip irrigation control using a hybrid wireless sensor and actuator network

In this research, a closed-loop drip irrigation control hybrid wireless sensor and actuator network (HWSAN) prototype were developed and deployed in a crop field for soil property precise measurement and precision irrigation in accordance with the measured soil property. The HWSAN was composed of a wireless sensor and actuator network (WSAN) used for in-field soil property monitoring and irrigation control and a laboratory supervising system. The WSAN included ten sensor nodes, five irrigation control nodes...     

Physics-Based Person Tracking Using the Anthropomorphic Walker

We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degree-of-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and spring-like forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset.     

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.     

A rapid computational filter for predicting the rate of human renal clearance

In silico models that predict the rate of human renal clearance for a diverse set of drugs, that exhibit both active secretion and net re-absorption, have been produced using three statistical approaches. Partial Least Squares (PLS) and Random Forests (RF) have been used to produce continuous models whereas Classification And Regression Trees (CART) has only been used for a classification model. The best models generated from either PLS or RF produce significant models that can predict acids/zwitterions, bases and neutrals with approximate average fold errors of 3, 3 and 4, respectively, for an independent test set that covers oral drug-like property space. These models contain additional information on top of any influence arising from plasma protein binding on the rate of renal clearance. Classification And Regression Trees (CART) has been used to generate a classification tree leading to a simple set of Renal Clearance Rules (RCR) that can be applied to man. The rules are influenced by lipophilicity and ion class and can correctly predict 60% of an independent test set. These percentages increase to 71% and 79% for drugs with renal clearances of < 0.1 ml/min/kg and > 1 ml/min/kg, respectively. As far as the authors are aware these are the first set of models to appear in the literature that predict the rate of human renal clearance and can be used to manipulate molecular properties leading to new drugs that are less likely to fail due to renal clearance.     

Tactical combat movements: inter-individual variation in performance due to the effects of load carriage

An examination into the effects of carried military equipment on the performance of two tactical combat movement simulations was conducted. Nineteen Airfield Defence Guards performed a break contact (five 30-m sprints) and a fire and movement simulation (16 6-m bounds) in five load conditions (10–30 kg). Heavier loads significantly increased movement duration on the break contact (0.8%/kg load) and fire and movement (1.1%/kg). Performance deterioration was observed from the beginning to the end of the series of movements (bounds or sprints) with deterioration becoming significantly greater in heavier load conditions. Inter-individual variation between slower and faster participants showed a range in load effects; 0.6, 0.8%/kg for fast and 1.0, 1.4%/kg for slow (break contact, fire and movement, respectively). Velocity profiles revealed that the initial acceleration and peak velocity were the primary determinants of performance. As the duration of these tactical combat movements reflects periods of heightened vulnerability, these findings highlight important implications for commanders.

Practitioner Summary: Increasing amounts of carried military equipment impairs the performance of tactical combat movements. Examination of inter-individual variation in velocity profiles identified that the initial acceleration and the peak velocity achieved during sprints and bounds are key determinants of overall performance.     

Quantifying blood flow and wall shear stresses in the outflow tract of chick embryonic hearts

Wall shear stresses (WSS) exerted by blood flow on cardiac tissues modulate growth and development of the heart. To study the role of hemodynamic conditions on cardiac morphogenesis, here, we present a methodology that combines imaging and finite element modeling to quantify the in vivo blood flow dynamics and WSS in the cardiac outflow tract (OFT) of early chicken embryos (day 3 out of 21-day incubation period). We found a distinct blood flow field and heterogeneous distribution of WSS in the chicken embryonic heart OFT, which have physiological implications for OFT morphogenesis.     

Modeling the glucose regulatory system in extreme preterm infants

Background

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

Methods

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4 h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N = 25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

Results

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

Conclusions

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.