Pest outbreaks have a significant impact on plants and crops, important to a reduction in national agricultural productivity. Conventionally, farmers and specialists rely on physical observation, and it can be timecon...
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Calcium imaging, as a means of high temporal and spatial resolution, has been widely used in neuroscience research to monitor the dynamic activity of neuronal networks. In this paper, a comprehensive analysis process ...
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ISBN:
(纸本)9798400712203
Calcium imaging, as a means of high temporal and spatial resolution, has been widely used in neuroscience research to monitor the dynamic activity of neuronal networks. In this paper, a comprehensive analysis process for calcium imaging data is proposed to improve the accuracy and effectiveness of data processing, and the analysis is performed using OASIS. First, Z-score standardization is applied to normalize the signal of each neuron to eliminate the difference in signal amplitude between different neurons. then, we design and implement a high-pass filtering method based on the frequency domain to extract high-frequency components and remove low-frequency noise and background signals by fast Fourier transform (FFT), thus enhancing the signal features of neuronal activity. the method was verified on two sets of calcium imaging data, and the results show that the signal after filtering is clearer and can capture the instantaneous activity and synchronization pattern of neurons more effectively. Finally, the result displays that the highly connected neurons in the primary visual cortex mainly interact with a specific population of neurons, indicating the existence of a specific local network structure. this study provides an effective tool for calcium imaging data analysis in the field of neuroscience and is expected to be widely used in the dynamic study of complex neural networks.
Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean a...
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ISBN:
(纸本)9798400712203
Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. the functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.
作者:
Shi, Justin Y.SMC Labs
630 Freedom Business Drive King of PrussiaPA19406 United States SERC 315
College of Science and Technology Temple University PhiladelphiaPA19122 United States
this paper proposes a blockchain-based high performance transaction processing system called TOIChain. A new programming paradigm and architecture using Active Content Addressable Networking protocol and Statistic Mul...
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Genetic Programming (GP), an evolutionary method, can be used to solve difficult problems in various applications. However, three important problems in GP are its tendency to find non-parsimonious solutions (bloat), t...
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ISBN:
(纸本)3540219463
Genetic Programming (GP), an evolutionary method, can be used to solve difficult problems in various applications. However, three important problems in GP are its tendency to find non-parsimonious solutions (bloat), to converge prematurely and to use a tremendous amount of computing time. In this paper, we present an efficient model of distributed GP to limit these general GP drawbacks. this model uses a multi-objective optimization and a hierarchical communication topology.
the rapid advancement of Internet of things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. this work proposes an IoT-based health monitoring system int...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
the rapid advancement of Internet of things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. this work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. these sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. the proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. the architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti
A new graph structuring algorithm for look-ahead reconfigurable multi-processor systems based on multiple crossbar switches is presented. It is based on list scheduling and a new iterative clustering heuristics for gr...
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ISBN:
(纸本)3540219463
A new graph structuring algorithm for look-ahead reconfigurable multi-processor systems based on multiple crossbar switches is presented. It is based on list scheduling and a new iterative clustering heuristics for graph partitioning. Look-ahead dynamic interprocessor connection reconfiguration is a multi-processor architectural model, which has been proposed to eliminate connection reconfiguration time overheads. It consists in preparing link connections in advance in parallel with program execution. An application program is partitioned into sections, which are executed using redundant communication resources, i.e. crossbar switches. parallel program scheduling in such a kind of environment incorporates graph partitioning problem. the experimental results are presented, which compare the performance of several graph partitioning algorithms for such a kind of environment.
In the paper a problem of minimizing the total completion time for deteriorating jobs and parallel identical machines is considered. the processing time of each job is a linear function of the starting time of the job...
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ISBN:
(纸本)3540219463
In the paper a problem of minimizing the total completion time for deteriorating jobs and parallel identical machines is considered. the processing time of each job is a linear function of the starting time of the job. the properties of an optimal schedule are proved and a greedy heuristic for the problem is proposed. Preliminary results of experimental evaluation of the algorithm are given.
A phylogenetic tree construction is one of the most important problems in computational biology. From computational point of view it is also one of the most difficult problem because of its intrinsic intractability. E...
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ISBN:
(纸本)3540219463
A phylogenetic tree construction is one of the most important problems in computational biology. From computational point of view it is also one of the most difficult problem because of its intrinsic intractability. Efficient algorithms are known only for some special cases of the problem which are unrealistic from biological point of view. Many algorithms are known for the problem, but since the problem is hard, they are usually heuristics. In this paper we present three exact parallel algorithms for the problem. they have been tested in computational experiment ran on SUN Fire computer.
In the paper the parallel algorithms of the Finite-Difference Time-Domain method are presented. those algorithms are based on the space domain decomposition. In the presented work, communications among computation nod...
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ISBN:
(纸本)3540219463
In the paper the parallel algorithms of the Finite-Difference Time-Domain method are presented. those algorithms are based on the space domain decomposition. In the presented work, communications among computation nodes in a cluster of PCs and the efficiency of the parallel algorithms are also discussed....
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