A binary self-organizing map and its FPGA implementation

A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features

A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement

A modified model for the Lobula Giant Movement Detector and its FPGA implementation

The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector

RF IC performance optimization by synthesizing optimum inductors

Even with optimal system design and careful choice of topology for a particular RF application, large amounts of energy are often wasted due to low-quality passives, especially inductors. Inductors have traditionally been difficult to integrate due to their inherent low quality factors and modelling complexity. Furthermore, although many different inductor configurations are available for an RF designer to explore, support for integrated inductors in electronic design automation tools and process design kits has been very limited in the past. In this chapter, a recent advance in technology-aware integrated inductor design is presented, where drawbacks of the integrated inductor design are addressed by introducing an equation-based inductor synthesis algorithm. The intelligent computation technique aims to allow RF designers to optimize integrated inductors, given the inductor center frequency dictated by the device application, and geometry constraints. This does not only lay down a foundation for system-level RF circuit performance optimization, but, because inductors are often the largest parts of an RF system, it also allows for optimal usage of chip real estate

Preferential Paths of Air-water Two-phase Flow in Porous Structures with Special Consideration of Channel Thickness Effects.

Accurate understanding and predicting the flow paths of immiscible two-phase flow in rocky porous structures are of critical importance for the evaluation of oil or gas recovery and prediction of rock slides caused by gas-liquid flow. A 2D phase field model was established for compressible air-water two-phase flow in heterogenous porous structures. The dynamic characteristics of air-water two-phase interface and preferential paths in porous structures were simulated. The factors affecting the path selection of two-phase flow in porous structures were analyzed. Transparent physical models of complex porous structures were prepared using 3D printing technology. Tracer dye was used to visually observe the flow characteristics and path selection in air-water two-phase displacement experiments. The experimental observations agree with the numerical results used to validate the accuracy of phase field model. The effects of channel thickness on the air-water two-phase flow behavior and paths in porous structures were also analyzed. The results indicate that thick channels can induce secondary air flow paths due to the increase in flow resistance; consequently, the flow distribution is different from that in narrow channels. This study provides a new reference for quantitatively analyzing multi-phase flow and predicting the preferential paths of immiscible fluids in porous structures

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization

pUL21 is a viral phosphatase adaptor that promotes herpes simplex virus replication and spread.

The herpes simplex virus (HSV)-1 protein pUL21 is essential for efficient virus replication and dissemination. While pUL21 has been shown to promote multiple steps of virus assembly and spread, the molecular basis of its function remained unclear. Here we identify that pUL21 is a virus-encoded adaptor of protein phosphatase 1 (PP1). pUL21 directs the dephosphorylation of cellular and virus proteins, including components of the viral nuclear egress complex, and we define a conserved non-canonical linear motif in pUL21 that is essential for PP1 recruitment. In vitro evolution experiments reveal that pUL21 antagonises the activity of the virus-encoded kinase pUS3, with growth and spread of pUL21 PP1-binding mutant viruses being restored in adapted strains where pUS3 activity is disrupted. This study shows that virus-directed phosphatase activity is essential for efficient herpesvirus assembly and spread, highlighting the fine balance between kinase and phosphatase activity required for optimal virus replication.Wellcome Trust Senior Research Fellowship (219447/Z/19/Z), Wellcome Trust Senior Research Fellowship (106207/Z/14/Z), Biotechnology and Biological Sciences Research Council Research Grant (BB/M021424/1), Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (098406/Z/12/B)

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

The common rs9939609 variant of the fat mass and obesity-associated gene is associated with obesity risk in children and adolescents of Beijing, China

<p>Abstract</p> <p>Background</p> <p>Previous genome-wide association studies for type 2 diabetes susceptibility genes have confirmed that a common variant, rs9939609, in the fat mass and obesity associated (<it>FTO</it>) gene region is associated with body mass index (BMI) in European children and adults. A significant association of the same risk allele has been described in Asian adult populations, but the results are conflicting. In addition, no replication studies have been conducted in children and adolescents of Asian ancestry.</p> <p>Methods</p> <p>A population-based survey was carried out among 3503 children and adolescents (6-18 years of age) in Beijing, China, including 1229 obese and 2274 non-obese subjects. We investigated the association of rs9939609 with BMI and the risk of obesity. In addition, we tested the association of rs9939609 with weight, height, waist circumference, waist-to-height ratio, fat mass percentage, birth weight, blood pressure and related metabolic traits.</p> <p>Results</p> <p>We found significant associations of rs9939609 variant with weight, BMI, BMI standard deviation score (BMI-SDS), waist circumference, waist-to-height ratio, and fat mass percentage in children and adolescents (<it>p </it>for trend = 3.29 × 10^-5, 1.39 × 10^-6, 3.76 × 10^-6, 2.26 × 10^-5, 1.94 × 10^-5, and 9.75 × 10^-5, respectively). No significant associations were detected with height, birth weight, systolic and diastolic blood pressure and related metabolic traits such as total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol and fasting plasma glucose (all <it>p </it>> 0.05). Each additional copy of the rs9939609 A allele was associated with a BMI increase of 0.79 [95% Confidence interval (CI) 0.47 to 1.10] kg/m², equivalent to 0.25 (95%CI 0.14 to 0.35) BMI-SDS units. This rs9939609 variant is significantly associated with the risk of obesity under an additive model [Odds ratio (OR) = 1.29, 95% CI 1.11 to 1.50] after adjusting for age and gender. Moreover, an interaction between the <it>FTO</it> rs9939609 genotype and physical activity (<it>p </it>< 0.001) was detected on BMI levels, the effect of rs9939609-A allele on BMI being (0.95 ± 0.10), (0.77 ± 0.08) and (0.67 ± 0.05) kg/m², for subjects who performed low, moderate and severe intensity physical activity.</p> <p>Conclusion</p> <p>The <it>FTO </it>rs9939609 variant is strongly associated with BMI and the risk of obesity in a population of children and adolescents in Beijing, China.</p