用离散增量结合支持向量机方法预测蛋白质亚细胞定位

对未知蛋白的功能注释是蛋白质组学的主要目标。一个关键的注释是蛋白质亚细胞定位的预测。本文应用离散增量结合支持向量机(ID_SVM)的方法,对阳性革兰氏细菌蛋白的5类亚细胞定位点进行预测。在独立检验下,其总体预测成功率为89.66%。结果发现ID_SVM算法对预测的成功率有很大改进。     

用支持向量机预测人类基因5′/3′选择性剪切位点

选择性剪切是调解基因表达的重要机制。识别选择性剪切位点是后基因组时代的一个重要工作。本文从最新的EBI人类基因选择性剪切数据库中,选取5′/3′选择性剪切位点作为正集,选取在剪切位点附近的假剪切位点作为负集,并把所有的选择性剪切位点和假剪切位点随机分成训练集和测试集。本文选用的预测选择性剪切位点的方法是基于位置权重矩阵和离散增量的支持向量机方法。此方法仅基于训练集,以不同位点的单碱基概率和序列片断的三联体频数作为信息参数,利用位置权重矩阵和离散增量算法结合支持向量机,得到了选择性供体位点和受体位点的分类器,并用此分类器对测试集中的选择性供体位点和受体位点进行预测。对独立测试集中的选择性供体位点和选择性受体位点的预测成功率分别为88．74%和90．86%,特异性分别为85．62%和81．19%。本文预测选择性剪切位点的方法成功率高于其它选择性剪切位点预测方法预测成功率,此预测方法进一步提高了对选择性剪切位点的理论预测能力。     

Fine detection of grasp force and posture by amputees via surface electromyography

The state-of-the-art feed-forward control of active hand prostheses is rather poor. Even dexterous, multi-fingered commercial prostheses are controlled via surface electromyography (EMG) in a way that enforces a few fixed grasping postures, or a very basic estimate of force. Control is not natural, meaning that the amputee must learn to associate, e.g., wrist flexion and hand closing. Nevertheless, recent literature indicates that much more information can be gathered from plain, old surface EMG. To check this issue, we have performed an experiment in which three amputees train a Support Vector Machine (SVM) using five commercially available EMG electrodes while asked to perform various grasping postures and forces with their phantom limbs. In agreement with recent neurological studies on cortical plasticity, we show that amputees operated decades ago can still produce distinct and stable signals for each posture and force. The SVM classifies the posture up to a precision of 95% and approximates the force with an error of as little as 7% of the signal range, sample-by-sample at 25 Hz. These values are in line with results previously obtained by healthy subjects while feed-forward controlling a dexterous mechanical hand. We then conclude that our subjects could finely feed-forward control a dexterous prosthesis in both force and position, using standard EMG in a natural way, that is, using the phantom limb.     

Flow injection amperometric detection of insulin at cobalt hydroxide nanoparticles modified carbon ceramic electrode

Cobalt hydroxide nanoparticles were prepared onto a carbon ceramic electrode (CHN|CCE) using the cyclic voltammetry (CV) technique. The modified electrode was characterized by X-ray diffraction and scanning electron microscopy. The results showed that CHN with a single-layer structure was uniformly electrodeposited on the surface of CCE. The electrocatalytic activity of the modified electrode toward the oxidation of insulin was studied by CV. CHN|CCE was also used in a homemade flow injection analysis system for insulin determination. The limit of detection (signal/noise [S/N] = 3) and sensitivity were found to be 0.11 nM and 11.8 nA/nM, respectively. Moreover, the sensor was used for detection of insulin in human serum samples. This sensor showed attractive properties such as high stability, reproducibility, and high selectivity.     

Development of a single-pass ceramic matrix bioreactor for large-scale mammalian cell culture

A single-pass, plug-flow bioreactor has been developed in which oxygen is supplied to entrapped hybridoma cells via sllicone tubes threaded through the square channels of a macroporous ceramic monolith. Oxygen diffuses from the gas phase, through the silicone tubing, across the open square channel, and into the pores of the ceramic wall where it is consumed by entrapped cells. Advantages of such a reactor include higher product yields, protection of cells from detrimental hydrodynamic effects, no internal moving parts to compromise asepsis, and simplicity of operation. A prototype bioreactor was constructed and operated over a range of residence times. A side-by-side experimental comparison with a conventional recycle bioreactor was performed by inoculating both bioreactors with cells from the same stock culture and feeding medium from the same reservoir. Final antibody titers were 80% higher in the single-pass bioreactor at a residence time of 200 minutes compared with those of the recycle bioreactor at a residence time of 800 minutes. A theoretical analysis of oxygen transport in this bioreactor is developed to highlight important design criteria and operating strategies for scale-up. (c) 1992 John Wiley & Sons, Inc.     

Cation exchange properties of porous ceramic cups: Implications for field use

Porous ceramic cup soil-water samplers were treated with solutions containing Na, K, Ca, or Mg to examine any interactions between the cups and the extracted solutions. Acid-washed and non-acid washed cups were evaluated. Results demonstrated simple ion exchange between monovalent and divalent cations on charged sites, and that Na contamination of commercially available cups was effectively removed by acid-washing.     

Classification of Early Stages of Esophageal Cancer Using Transfer Learning

ObjectivesEsophageal Cancer is the sixth most common cancer with a high fatality rate. Early prognosis of esophageal abnormalities can improve the survival rate of the patients. The sequence of the progress of the esophageal cancer is from esophagitis to non-dysplasia Barrett's esophagus to dysplasia Barrett's esophagus to esophageal adenocarcinoma (EAC). Many studies revealed a 5-fold increase in EAC patients diagnosed with esophagitis, and those diagnosed with Barrett's esophagus have a greater risk of EAC.Material and methodsConvolutional Neural Network (CNN) with efficient feature extractors enable better prognosis of the pre cancerous stage, Barrett's esophagus and esophagitis. The transfer learning techniques with CNN can extract more relevant features for the automated classification of Barrett's esophagus and esophagitis. This paper presents a study on the classification of the esophagitis and Barrett's esophagus (BE) using Deep Convolution Neural Networks (DCNN).ResultsIn the first experiment, the DCNN models perform as a feature extractor, and standard classifiers do the classification. The performance analysis shows that the CNN model ResNet50 with Support Vector Machine (SVM) has an accuracy of 93.5%, recall 93.5%, precision 93.4%, f score 93.5%, AUC 89.8%. In the second experiment, the DCNN classification models perform the classification with Transfer Learning and fine-tuning. The ResNet50 model has improved accuracy of 94.46%, precision 94.46%, f score 94.46%, AUC 96.20%.ConclusionThe ResNet50 model with transfer learning and fine-tuning gives a better performance than the ResNet50 model with SVM classifier. Our experiments show that the DCNN is effective for diagnosing EAC, both as feature extractors and classification models with transfer learning and fine-tuning.     

Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah,Malaysia

Vocal individuality has been documented in a variety of mammalian species and it has been proposed that this individuality can be used as a vocal fingerprint to monitor individuals. Here we provide and test a classification method using Mel-frequency cepstral coefficients (MFCCs) to extract features from Bornean gibbon female calls. Our method is semi-automated as it requires manual pre-processing to identify and extract calls from the original recordings. We compared two methods of MFCC feature extraction: (1) averaging across all time windows and (2) creating a standardized number of time windows for each call. We analysed 376 calls from 33 gibbon females and, using linear discriminant analysis, found that we were able to improve female identification accuracy from 95.7% with spectrogram features to 98.4% accuracy when averaging MFCCs across time windows, and 98.9% accuracy when using a standardized number of windows. We divided our data randomly into training and test data-sets, and tested the accuracy of support vector machine (SVM) predictions over 100 iterations. We found that we could predict female identity in the test data-set with a 98.8% accuracy. Using SVM on our entire data-set, we were able to predict female identity with 99.5% accuracy (validated by leave-one-out cross-validation). Lastly, we used the method presented here to classify four females recorded during three or more recording seasons using SVM with limited success. We provide evidence that MFCC feature extraction is effective for distinguishing between female Bornean gibbons, and make suggestions for future vocal fingerprinting applications.     

Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.