Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.
Rapid and sensitive point-of-care testing (POCT) is an extremely critical mission in practical applications, especially for rigorous military medicine, home health care, and in the third world. Here, we report a visual POCT method for adenosine triphosphate (ATP) detection based on Taylor rising in the corner of quadratic geometries between two rod surfaces. We discuss the principle of Taylor rising, demonstrating that it is significantly influenced by contact angle, surface tension, and density of the sample, which are controlled by ATP-dependent rolling circle amplification (RCA). In the presence of ATP, RCA reaction effectively suppresses Taylor-rising behavior, due to the increased contact angle, density, and decreased surface tension. Without addition of ATP, untriggered RCA reaction is favorable for Taylor rising, resulting in a significant height. With this proposed method, visual sensitive detection of ATP without the aid of other instruments is realized with only a 5 μL droplet, which has good selectivity and a low detection limit (17 nM). Importantly, this visual method provides a promising POCT tool for user-friendly molecular diagnostics. 相似文献
Due to their small size, mammalian oocytes and embryos pose unique problems during preparation for transmission electron microscopy. This paper outlines a method which combines protein embedding with centrifugation to locate the specimens on the face of a Beem capsule mould. This method facilitates both the processing of oocytes with minimal loss and rapid location of the specimens within the block for simultaneous sectioning, staining and examination. 相似文献
Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions
in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions.
Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such
applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we
used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their
discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless
sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show
that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for
on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to
schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes
and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results
show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared
to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput.
As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery
model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless
networks as well. 相似文献
Gesture plays an important role for recognizing lecture activities in video content analysis. In this paper, we propose a real-time gesture detection algorithm by integrating cues from visual, speech and electronic slides. In contrast to the conventional “complete gesture” recognition, we emphasize detection by the prediction from “incomplete gesture”. Specifically, intentional gestures are predicted by the modified hidden Markov model (HMM) which can recognize incomplete gestures before the whole gesture paths are observed. The multimodal correspondence between speech and gesture is exploited to increase the accuracy and responsiveness of gesture detection. In lecture presentation, this algorithm enables the on-the-fly editing of lecture slides by simulating appropriate camera motion to highlight the intention and flow of lecturing. We develop a real-time application, namely simulated smartboard, and demonstrate the feasibility of our prediction algorithm using hand gesture and laser pen with simple setup without involving expensive hardware. 相似文献