Digital versus analog PET/CT in patients with known or suspected liver metastases.

AIM: To assess if digital PET/CT improves liver lesion detectability compared to analog PET/CT in patients with known or suspected liver metastases. MATERIALS AND METHODS: We prospectively included 83 cancer patients, with one or more of these conditions: history of liver metastases, clinical risk of having liver metastases or presence of suspected liver metastases on the first of the two PET/CTs. All patients were consecutively scanned on each PET/CT on the same day after a single [18F]fluorodeoxyglucose dose injection. The order of acquisition was randomly assigned. Three nuclear medicine physicians assessed both PET/CTs by counting the foci of high uptake suspicious of liver metastases. Findings were correlated with appropriate reference standards; 19 patients were excluded from the analysis due to insufficient lesion nature confirmation. The final sample consisted of 64 patients (34 women, mean age 68 ± 12 years). RESULTS: As per-patient analysis, the mean number of liver lesions detected by the digital PET/CT (3.84 ± 4.25) was significantly higher than that detected by the analog PET/CT (2.91 ± 3.31); P < 0.001. Fifty-five patients had a positive PET/CT study for liver lesions. In 26/55 patients (47%), the digital PET/CT detected more lesions; 7/26 patients (27%) had detectable lesions only by the digital system and had <10 mm of diameter. Twenty-nine patients had the same number of liver lesions detected by both systems. In nine patients both PET/CT systems were negative for liver lesions. CONCLUSION: Digital PET/CT offers improved detectability of liver lesions over the analog PET/CT in patients with known or suspected liver metastases.

Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces.

Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.

A digital data interpretation method for hemagglutination inhibition assay by using a plate reader.

Hemagglutination inhibition (HAI) assay is a simple method quantifying relative binding activities of glycan-lectin interactions. Currently, interpretation of HAI data remains a manual task depending on visual observation. In this study we developed a digital data reading method for HAI assay by using the area scanning function of a microplate reader. This was based on galectin-3-induced hemagglutination inhibition assay. OD values showed a four-parameter logistic correlation with concentrations of galectin-3 inhibitors (R2 ≥ 0.97), and IC50 values were obtained from the curve fitting. The method provides an objective and robust data interpretation for HAI assays conducted with chicken erythrocytes.

Odontología Digital: El futuro es ahora / Digital Dentistry: The future is now

La práctica clínica de la odontología avanzó desde los últimos 20 años, con la inmersión del CAD/CAM (Computer-assisted Design and Manufacturing), reduciendo tanto los pasos para la realización de una corona o prótesis, como mejorando la eficiencia, calidad del tratamiento y por consiguiente, la experiencia percibida por los pacientes. En Estados Unidos se estima que, el 15% de los consultorios practican Odontología Digital, es decir, hacen uso de tecnología CAD/CAM e impresión digital 3D. Además, para el año 2017, se estimó el uso de 19,000 unidades de CAD/CAM en las oficinas dentales de odontólogos americanos, mostrando que, los avances de la tecnología que veíamos muy lejanos, son una realidad, hoy (1,2). A pesar de estas cifras, las universidades han adoptado el entrenamiento en tecnología digital odontológica tímidamente, tal como lo han manifestado, según el entusiasmo por parte de docentes y estudiantes. Esto se ve reflejado en la incipiente publicación de artículos científicos en ésta área. La odontología digital se ha desarrollado en varios campos dentro de la odontología. De hecho, se ha utilizado desde el diagnóstico, planificación del tratamiento, hasta el diseño y elaboración de prótesis y restauraciones. Dentro de las herramientas de la odontología digital, se encuentra el escáner intraoral. Los escáneres han permitido capturar información detallada de las estructuras anatómicas dentales, óseas y tejidos blandos del paciente. Por tanto, es una herramienta útil para el diagnóstico y planificación del tratamiento del paciente. Además, los datos pueden ser transferidos para una impresora 3D, permitiendo obtener modelos de estudios de los pacientes sin necesidad de tomar una impresión en hidrocoloide irreversible (alginato) o en silicona. En el área de Periodoncia, la odontología digital ofrece ventajas para el estudio de los sitios quirúrgicos a operar tanto en procedimientos resectivos como regenerativos. De esta forma, la regeneración ósea guiada, la instalación quirúrgica de implantes dentales, entre otros, son procedimientos que serán realizados con mayor supervisión del clínico, haciendo la cirugía más precisa, predecible y segura para el paciente. Por su parte, la odontología pediátrica y ortopedia maxilar ha sido enriquecida en el diagnóstico de pacientes con labio y paladar fisurado, una vez que se puede evitar el uso de materiales como el hidrocoloide irreversible (alginato), disminuyendo la incomodidad que puede causar para el paciente pediátrico, sobre todo en aquellos pacientes con labio y paladar hendido. De otro lado, la ortodoncia ha utilizado la tecnología digital para personalizar los brackets de los pacientes, permitiendo un mayor control de las fuerzas ortodóncicas y, aumentando la eficacia del movimiento dental. Esto se verá traducido en menor tasa de reabsorción radicular, menor tiempo del tratamiento ortodóncico, entre otras ventajas. Finalmente, la odontología digital puede ser explotada para la educación de los pacientes. De hecho, el éxito del tratamiento no solo depende de los procedimientos realizados por el odontólogo sino también por los cuidados que el paciente tenga después de su atención clínica. Así, el uso de una impresión 3D generada a partir de información obtenida desde un escáner intraoral o una tomografía, permite mostrarle al paciente la relación entre el diagnóstico y el plan de tratamiento elegido. Adicionalmente, el paciente puede ser consciente de la evolución de su cicatrización después de la realización del tratamiento. Con todo esto, aún falta mucho por explorar. La era digital en odontología representa una atractiva línea de investigación, para aquellas personas inquietas que buscan mejorar cada vez más los servicios odontológicos. Así, esperamos comenzar en la Universidad del Valle, un camino, una trayectoria para ser pioneros en la odontología digital.

Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

In socially important visual search tasks, such as baggage screening and diagnostic radiology, experts miss more targets than is desirable. Computer-aided detection (CAD) programs have been developed specifically to improve performance in these professional search tasks. For example, in breast cancer screening, many CAD systems are capable of detecting approximately 90% of breast cancer, with approximately 0.5 false-positive detections per image. Nevertheless, benefits of CAD in clinical settings tend to be small (Birdwell, 2009) or even absent (Meziane et al., 2011; Philpotts, 2009). The marks made by a CAD system can be "binary," giving the same signal to any location where the signal is above some threshold. Alternatively, a CAD system presents an analog signal that reflects strength of the signal at a location. In the experiments reported, we compare analog and binary CAD presentations using nonexpert observers and artificial stimuli defined by two noisy signals: a visible color signal and an "invisible" signal that informed our simulated CAD system. We found that analog CAD generally yielded better overall performance than binary CAD. The analog benefit is similar at high and low target prevalence. Our data suggest that the form of the CAD signal can directly influence performance. Analog CAD may allow the computer to be more helpful to the searcher.

Structural Modeling of GR Interactions with the SWI/SNF Chromatin Remodeling Complex and C/EBP.

The glucocorticoid receptor (GR) is a steroid-hormone-activated transcription factor that modulates gene expression. Transcriptional regulation by the GR requires dynamic receptor binding to specific target sites located across the genome. This binding remodels the chromatin structure to allow interaction with other transcription factors. Thus, chromatin remodeling is an essential component of GR-mediated transcriptional regulation, and understanding the interactions between these molecules at the structural level provides insights into the mechanisms of how GR and chromatin remodeling cooperate to regulate gene expression. This study suggests models for the assembly of the SWI/SNF-A (SWItch/Sucrose-NonFermentable) complex and its interaction with the GR. We used the PRISM algorithm (PRotein Interactions by Structural Matching) to predict the three-dimensional complex structures of the target proteins. The structural models indicate that BAF57 and/or BAF250 mediate the interaction between the GR and the SWI/SNF-A complex, corroborating experimental data. They further suggest that a BAF60a/BAF155 and/or BAF60a/BAF170 interaction is critical for association between the core and variant subunits. Further, we model the interaction between GR and CCAAT-enhancer-binding proteins (C/EBPs), since the GR can regulate gene expression indirectly by interacting with other transcription factors like C/EBPs. We observe that GR can bind to bZip domains of the C/EBPα homodimer as both a monomer and dimer of the DNA-binding domain. In silico mutagenesis of the predicted interface residues confirm the importance of these residues in binding. In vivo analysis of the computationally suggested mutations reveals that double mutations of the leucine residues (L317D+L335D) may disrupt the interaction between GR and C/EBPα. Determination of the complex structures of the GR is of fundamental relevance to understanding its interactions and functions, since the function of a protein or a complex is dictated by its structure. In addition, it may help us estimate the effects of mutations on GR interactions and signaling.

Back-propagation operation for analog neural network hardware with synapse components having hysteresis characteristics.

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware.

Bio-inspired modeling and implementation of the ocelli visual system of flying insects.

Two visual sensing modalities in insects, the ocelli and compound eyes, provide signals used for flight stabilization and navigation. In this article, a generalized model of the ocellar visual system is developed for a 3-D visual simulation environment based on behavioral, anatomical, and electrophysiological data from several species. A linear measurement model is estimated from Monte Carlo simulation in a cluttered urban environment relating state changes of the vehicle to the outputs of the ocellar model. A fully analog-printed circuit board sensor based on this model is designed and fabricated. Open-loop characterization of the sensor to visual stimuli induced by self motion is performed. Closed-loop stabilizing feedback of the sensor in combination with optic flow sensors is implemented onboard a quadrotor micro-air vehicle and its impulse response is characterized.

A generalized analog implementation of piecewise linear neuron models using CCII building blocks.

This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology.

A programmable analog subthreshold biomimetic model for bi-directional communication with the brain.

In this paper, we present a hardware implementation of a second order Laguerre Expansion of Volterra Kernel (LEV) model with four basis functions. The model is versatile enough to be applied at different abstraction levels (synapse, neuron, or network of neurons) and is implemented with analog building blocks in a modular manner. These analog blocks, realized using low power subthreshold CMOS transistors, can serve as a basis for large-scale hardware systems that emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error between the signals produced by the circuit LEV implementation and the ideal LEV model is 8.15%. The total power consumption of the analog circuitry is less than 33nW.

Synthetic analog computation in living cells.

A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber's law behaviour as in natural biological systems, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression.

Configurable hardware integrate and fire neurons for sparse approximation.

Sparse approximation is an important optimization problem in signal and image processing applications. A Hopfield-Network-like system of integrate and fire (IF) neurons is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete L1 sparse approximation problem. A scalable system architecture is described, including IF neurons with a nonlinear firing function, and current-based synapses to provide linear computation. A network of 18 neurons with 12 inputs is implemented on the RASP 2.9v chip, a Field Programmable Analog Array (FPAA) with directly programmable floating gate elements. Said system uses over 1400 floating gates, the largest system programmed on a FPAA to date. The circuit successfully reproduced the outputs of a digital optimization program, converging to within 4.8% RMS, and an objective cost only 1.7% higher on average. The active circuit consumed 559 µA of current at 2.4 V and converges on solutions in 25 µs, with measurement of the converged spike rate taking an additional 1 ms. Extrapolating the scaling trends to a N=1000 node system, the spiking LCA compares favorably with state-of-the-art digital solutions, and analog solutions using a non-spiking approach.

Power-efficient simulation of detailed cortical microcircuits on SpiNNaker.

Computer simulation of neural matter is a promising methodology for understanding the function of the brain. Recent anatomical studies have mapped the intricate structure of cortex, and these data have been exploited in numerous simulations attempting to explain its function. However, the largest of these models run inconveniently slowly and require vast amounts of electrical power, which hinders useful experimentation. SpiNNaker is a novel computer architecture designed to address these problems using low-power microprocessors and custom communication hardware. We use four SpiNNaker chips (of a planned fifty thousand) to simulate, in real-time, a cortical circuit of ten thousand spiking neurons and four million synapses. In this simulation, the hardware consumes 100 nJ per neuron per millisecond and 43 nJ per postsynaptic potential, which is the smallest quantity reported for any digital computer. We argue that this approaches fast, power-feasible and scientifically useful simulations of large cortical areas.

Bootstrap methods for comparing independent regression slopes.

In this study, we explore the effects of non-normality and heteroscedasticity when testing the hypothesis that the regression lines associated with multiple independent groups have the same slopes. The conventional approach involving the F-test and the t-test (F/t approach) is examined. In addition, we introduce two robust methods which allow simultaneous testing of regression slopes. Our results suggest that the F/t approach is extremely sensitive to violations of assumptions and tends to yield misleading conclusions. The new robust alternatives are recommended for general use.

Reduction of magnetic field fluctuations in powered magnets for NMR using inductive measurements and sampled-data feedback control.

Resistive and hybrid (resistive/superconducting) magnets provide substantially higher magnetic fields than those available in low-temperature superconducting magnets, but their relatively low spatial homogeneity and temporal field fluctuations are unacceptable for high resolution NMR. While several techniques for reducing temporal fluctuations have demonstrated varying degrees of success, this paper restricts attention to methods that utilize inductive measurements and feedback control to actively cancel the temporal fluctuations. In comparison to earlier studies using analog proportional control, this paper shows that shaping the controller frequency response results in significantly higher reductions in temporal fluctuations. Measurements of temporal fluctuation spectra and the frequency response of the instrumentation that cancels the temporal fluctuations guide the controller design. In particular, we describe a sampled-data phase-lead-lag controller that utilizes the internal model principle to selectively attenuate magnetic field fluctuations caused by the power supply ripple. We present a quantitative comparison of the attenuation in temporal fluctuations afforded by the new design and a proportional control design. Metrics for comparison include measurements of the temporal fluctuations using Faraday induction and observations of the effect that the fluctuations have on nuclear resonance measurements.

Tunable complex-valued multi-tap microwave photonic filter based on single silicon-on-insulator microring resonator.

A complex-valued multi-tap tunable microwave photonic filter based on single silicon-on-insulator microring resonator is presented. The degree of tunability of the approach involving two, three and four taps is theoretical and experimentally characterized, respectively. The constraints of exploiting the optical phase transfer function of a microring resonator aiming at implementing complex-valued multi-tap filtering schemes are also reported. The trade-off between the degree of tunability without changing the free spectral range and the number of taps is studied in-depth. Different window based scenarios are evaluated for improving the filter performance in terms of the side-lobe level.

The Neurochip-2: an autonomous head-fixed computer for recording and stimulating in freely behaving monkeys.

The Neurochip-2 is a second generation, battery-powered device for neural recording and stimulating that is small enough to be carried in a chamber on a monkey's head. It has three recording channels, with user-adjustable gains, filters, and sampling rates, that can be optimized for recording single unit activity, local field potentials, electrocorticography, electromyography, arm acceleration, etc. Recorded data are stored on a removable, flash memory card. The Neurochip-2 also has three separate stimulation channels. Two "programmable-system-on-chips" (PSoCs) control the data acquisition and stimulus output. The PSoCs permit flexible real-time processing of the recorded data, such as digital filtering and time-amplitude window discrimination. The PSoCs can be programmed to deliver stimulation contingent on neural events or deliver preprogrammed stimuli. Access pins to the microcontroller are also available to connect external devices, such as accelerometers. The Neurochip-2 can record and stimulate autonomously for up to several days in freely behaving monkeys, enabling a wide range of novel neurophysiological and neuroengineering experiments.

Analysis and compensation of the effects of analog VLSI arithmetic on the LMS algorithm.

Analog very large scale integration implementations of neural networks can compute using a fraction of the size and power required by their digital counterparts. However, intrinsic limitations of analog hardware, such as device mismatch, charge leakage, and noise, reduce the accuracy of analog arithmetic circuits, degrading the performance of large-scale adaptive systems. In this paper, we present a detailed mathematical analysis that relates different parameters of the hardware limitations to specific effects on the convergence properties of linear perceptrons trained with the least-mean-square (LMS) algorithm. Using this analysis, we derive design guidelines and introduce simple on-chip calibration techniques to improve the accuracy of analog neural networks with a small cost in die area and power dissipation. We validate our analysis by evaluating the performance of a mixed-signal complementary metal-oxide-semiconductor implementation of a 32-input perceptron trained with LMS.