一种复合神经元网络结构的并行动力学及快速学习算法

提出两种功能互相不同的神经细胞组成的复合神经元网络(CNN)模型;导出一种特殊结构的CNN的并行动力学;而且证明了它的稳定性。在这些结果基础上,得到快速的假逆矩阵学习算法。计算机仿真试验证实学习算法与动力学稳定性的正确性,并表现出良好的容错性能与存储容量。     

模块神经网络及其性能

提出一个不同于实现ｙ＝ｆ（ｘ）的ＢＰ网络的神经网络模型,给出了网络的结构及并行动力学方程,证明了其动力学的稳定性。通过学习算法的建立,证明网络能精确实现输入矢量对（ｘ,ｙ）映入成相联系的输出矢量ｚ,最重要的是网络能同时存诸依时变化的时序模式与静态模式。此外并给出动力学学习算法,证明此学习算法的收敛性,计算机仿真证实理论结果,最后讨论了某些可能的应用。     

基于卷积神经网络的大肠杆菌启动子预测

目的 基于位点特异性打分矩阵（position-specific scoring matrices,PSSM）的预测模型已经取得了良好的效果,基于PSSM的各种优化方法也在不断发展,但准确率相对较低,为了进一步提高预测准确率,本文基于卷积神经网络（convolutional neural networks,CNN）算法做了进一步研究。方法 采用PSSM将启动子序列处理成数值矩阵,通过CNN算法进行分类。大肠杆菌K-12（Escherichia coli K-12,E.coli K-12,下文简称大肠杆菌）的Sigma38、Sigma54和Sigma70 3种启动子序列被作为正集,编码（Coding）区和非编码（Non-coding）区的序列为负集。结果 在预测大肠杆菌启动子的二分类中,准确率达到99%,启动子预测的成功率接近100%;在对Sigma38、Sigma54、Sigma70 3种启动子的三分类中,预测准确率为98%,并且针对每一种序列的预测准确率均可以达到98%以上。最后,本文以Sigma38、Sigma54、Sigma70 3种启动子分别和Coding区或者Non-coding区序列做四分类,预测得到的准确性为0.98,对3种Sigma启动子均衡样本的十交叉检验预测精度均可以达到0.95以上,海明距离为0.016,Kappa系数为0.97。结论 相较于支持向量机（support vector machine,SVM）等其他分类算法,CNN分类算法更具优势,并且基于CNN的分类优势,编码方式亦可以得到简化。     

高阶Boltzmann机的平均场理论学习算法

用统计力学中的平均场理论及模拟退火技术,将一般高阶神经网络及玻尔兹曼机的优点结合在一起,以不同于文献［２］的方法,推导出高阶玻尔兹曼机的驰豫动力学的确定性方程和平均场理论学习算法。二者皆便于ＶＬＳＩ实现,且学习算法省去很多ＣＰＵ时间,对二维镜像对称问题及Ｔ－问题的计算机仿真结果表明三阶玻尔兹曼机的平均场理论学习算法是正确的,且性能较二阶玻尔兹曼机好。     

不同酿酒酵母菌株来源异麦芽糖酶IMA1的克隆、表达及表征

[目的]异麦芽糖酶IMA1在充分利用含有α-1,6-O-糖苷键的低聚糖中起着关键作用。[方法]在本研究中,对来自4株酿酒酵母菌株（包括3株嗜酸性菌株）来源的异麦芽糖酶IMA1进行克隆、表达、纯化和表征。[结果]研究发现,4种异麦芽糖酶IMA1表现出类似的pH和温度依赖性,但表现出不同的动力学参数和热稳定性。IMA1-A对α-MG（α-甲基葡糖苷）表现出最高的结合亲和力、转换数、催化效率和热稳定性。结构和序列分析表明,2个远离活性位点和底物结合位点的氨基酸的差异对异麦芽糖酶IMA1的动力学参数和热稳定性有重要影响。[结论]本研究结果对进一步研究异麦芽糖酶IMA1的结构-功能关系奠定了基础。     

嗜酸氧化亚铁硫杆菌生长动力学方程的应用

基于Monod模型推导出了A．f的生长动力学方程模型,采用Gauss-Newton算法确定了在不同初始条件下细菌生长的动力学参数,即最大比生长速率‰、Monod常数K及R0。通过在不同初始条件下细菌生长特性的研究,得到了相应初始生长条件下以限制性底物亚铁离子浓度为表征的生长动力学方程,理论上揭示了动力学参数变化对细菌生长的影响规律,其中生长动力学方程的数值模拟与实验数据相吻合。     

建德县南峰乡丘陵山地开发利用的生态经济规划研究Ⅱ．不同决策方案的系统模拟

采用系统动力学方法对6种不同的决策方案进行动态模拟和灵敏度分析,预测和分析各种决策方案的系统生产力、稳定性和持续性。结果表明,多目标规划型的系统生产力较高稳定性和持续性较好,是一种较优的选择方案;现状型的系统生产力较低,林主型的生产力很高,但经济产出水平低,柑桔、板栗和蚕桑型的系统生产力高,但系统的稳定性和持续性较差。     

分批发酵动力学模型参数的估算

本文基于通用的发酵动力学数学模型,导出了用于描述分批发酵特征的解析解。藉助于由FORTRAN－77编写的POWELL优化算法,以赖氨酸分批发酵为倒^［5］,一举估算出该解析解中所有的发酵动力学参数;μ_max、K_s、α、β、Y_G、Y_p,及m。结果表明t(1)用模型所得到的计算值与实测值具有较好的一致性,(2)赖氨酸合成速度取决于微生物的生长速度及浓度。     

适用于大小动物的一种飞行模式

人们对飞行能力非常羡慕。飞行的才能使得许多动物能够经由空气来旅行,但有关动物的这种特殊的灵动性和稳定性的详情人们却知之甚少。如今,研究人员已经研发出了预测空中旋转动力学的框架结构,并能用它来预测体型大小不同的7种不同种类飞行动物的飞行运动。     

不同酿酒酵母菌株来源异麦芽糖酶IMA1的克隆、表达及表征（英文）

【目的】异麦芽糖酶IMA1在充分利用含有α-1,6-O-糖苷键的低聚糖中起着关键作用。【方法】在本研究中,对来自4株酿酒酵母菌株(包括3株嗜酸性菌株)来源的异麦芽糖酶IMA1进行克隆、表达、纯化和表征。【结果】研究发现,4种异麦芽糖酶IMA1表现出类似的pH和温度依赖性,但表现出不同的动力学参数和热稳定性。IMA1-A对α-MG(α-甲基葡糖苷)表现出最高的结合亲和力、转换数、催化效率和热稳定性。结构和序列分析表明,2个远离活性位点和底物结合位点的氨基酸的差异对异麦芽糖酶IMA1的动力学参数和热稳定性有重要影响。【结论】本研究结果对进一步研究异麦芽糖酶IMA1的结构-功能关系奠定了基础。     

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.     

A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images

Plant diseases cause significant food loss and hence economic loss around the globe. Therefore, automatic plant disease identification is a primary task to take proper medications for controlling the spread of the diseases. Large variety of plants species and their dissimilar phytopathological symptoms call for the implementation of supervised machine learning techniques for efficient and reliable disease identification and classification. With the development of deep learning strategies, convolutional neural network (CNN) has paved its way for classification of multiple plant diseases by extracting rich features. However, several characteristics of the input images especially captured in real world environment, viz. complex or indistinguishable background, presence of multiple leaves with the diseased leaf, small lesion area, solemnly affect the robustness and accuracy of the CNN modules. Available strategies usually applied standard CNN architectures on the images captured in the laboratory environment and very few have considered practical in-field leaf images for their studies. However, those studies are limited with very limited number of plant species. Therefore, there is need of a robust CNN module which can successfully recognize and classify the dissimilar leaf health conditions of non-identical plants from the in-field RGB images. To achieve the above goal, an attention dense learning (ADL) mechanism is proposed in this article by merging mixed sigmoid attention learning with the basic dense learning process of deep CNN. The basic dense learning process derives new features at higher layer considering all lower layer features and that provides fast and efficient training process. Further, the attention learning process amplifies the learning ability of the dense block by discriminating the meaningful lesion portions of the images from the background areas. Other than adding an extra layer for attention learning, in the proposed ADL block the output features from higher layer dense learning are used as an attention mask to the lower layers. For an effective and fast classification process, five ADL blocks are stacked to build a new CNN architecture named DADCNN-5 for obtaining classification robustness and higher testing accuracy. Initially, the proposed DADCNN-5 module is applied on publicly available extended PlantVillage dataset to classify 38 different health conditions of 14 plant species from 54,305 images. Classification accuracy of 99.93% proves that the proposed CNN module can be used for successful leaf disease identification. Further, the efficacy of the DADCNN-5 model is checked after performing stringent experiments on a new real world plant leaf database, created by the authors. The new leaf database contains 10,851 real-world RGB leaf images of 17 plant species for classifying their 44 distinguished health conditions. Experimental outcomes reveal that the proposed DADCNN-5 outperforms the existing machine learning and standard CNN architectures, and achieved 97.33% accuracy. The obtained sensitivity, specificity and false positive rate values are 96.57%, 99.94% and 0.063% respectively. The module takes approximately 3235 min for training process and achieves 99.86% of training accuracy. Visualization of Class activation mapping (CAM) depicts that DADCNN-5 is able to learn distinguishable features from semantically important regions (i.e. lesion regions) on the leaves. Further, the robustness of the DADCNN-5 is established after experimenting with augmented and noise contaminated images of the practical database.     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large-scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state-of-the-art methods and has a high practical application value.     

Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.     

CNN3 Is Regulated by microRNA-1 during Muscle Development in Pigs

The calponin 3 (CNN3) gene has important functions involved in skeletal muscle development. MicroRNAs (miRNAs) play critical role in myogenesis by influencing the mRNA stability or protein translation of target gene. Based on paired microRNA and mRNA profiling in the prenatal skeletal muscle of pigs, our previous study suggested that CNN3 was differentially expressed and a potential target for miR-1. To further understand the biological function and regulation mechanism of CNN3, we performed co-expression analysis of CNN3 and miR-1 in developmental skeletal muscle tissues (16 stages) from Tongcheng (a Chinese domestic breed, obese-type) and Landrace (a Western, lean-type) pigs, respectively. Subsequently, dual luciferase and western blot assays were carried out. During skeletal muscle development, we observe a significantly negative expression correlation between the miR-1 and CNN3 at mRNA level. Our dual luciferase and western blot results suggested that the CNN3 gene was regulated by miR-1. We identified four single nucleotide polymorphisms (SNPs) contained within the CNN3 gene. Association analysis indicated that these CNN3 SNPs are significantly associated with birth weight (BW) and the 21-day weaning weight of the piglets examined. These facts indicate that CNN3 is a candidate gene associated with growth traits and regulated by miR-1 during skeletal muscle development in pigs.     

Tumor Cell Identification in Ki-67 Images on Deep Learning

The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on estimating the probability heatmap of tumor cells using CNN with accuracy of 98% and detecting the tumor cells using SSD with accuracy of 90%. This deep learning framework will provide an objective basis for the malignant degree of breast tumors and be beneficial to the pathologists for fast and efficiently Ki-67 scoring.