固定化酶的微环境效应—载体离子基团性质的影响

作者合成了阴离子型和阳离子型葡聚糖,以此为载体,用CNBr活化其剩余羟基,固定化了葡萄糖淀粉酶和葡萄糖异构酶。就离子型载体对固定化酶的蛋白载量、最适pH和热稳定性等的影响做了考察。发现固定化酶的蛋白载量不仅与载体的电性质有关,也与酶分子自身的电性质有关。当载体电性质与酶蛋白电性质相反时,固定化酶的蛋白载量增加,热稳定性提高、载体电性质与酶蛋白电性质相同时,固定化酶的蛋白载量不变或下降,其热稳定性不变。作者还发现当离子型载体孔度和体系缓冲液浓度一定时,酶分子能否进入多孔性载体内部,对其最适pH是否变化影响极大。若酶分子仅被连接在载体的外表层,其最适pH不发生变化,反之亦然。作者还观察到当多糖类载体引入氨基或羧基后,大大增强了其抵抗微生物侵蚀的能力。     

一种融合抗体ScFv-Fc通用表达载体的构建

为了构建一个可供自由替换的ScFv区,表达人小分子融合抗体ScFv-Fc的通用载体,利用RT-PCR技术扩增人抗体IgG1的Fc片段克隆至毕赤酵母表达载体pPICZα,将一段人工合成的互补寡核苷酸链插入重组载体pPICZα/Fc中Fc区的上游,引入2个可供小分子抗体ScFv-Fc的ScFv区自由替换的限制性酶切位点。分别扩增人抗狂犬病毒以及抗乙型肝炎表面抗原的ScFv片段,克隆至已构建的通用载体pPICZα/Fc,在毕赤酵母中诱导表达。进一步在1L条件下对活性抗体进行发酵,并利用protein A亲和层析柱进行纯化。应用酵母基因组PCR、ELISA、Western blotting、活性检测等试验对此小分子抗体的表达进行生物学及免疫学分析。结果表明具有狂犬病毒抗原结合活性以及乙肝表面抗原结合活性的人源抗体分子均获得成功表达,1L发酵条件下表达量达到20~30mg/L, protein A亲和层析纯化后纯度＞95%。研究构建了可用于功能性抗体分子ScFv-Fc筛选和表达的通用载体并对其发酵、纯化条件进行了摸索,为重组抗体分子诊断、治疗试剂的开发以及抗体的人源化奠定了物质基础。     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.     

Identification of Penicillin-binding proteins employing support vector machines and random forest

Penicillin-Binding Proteins are peptidases that play an important role in cell-wall biogenesis in bacteria and thus maintaining bacterial infections. A wide class of β-lactam drugs are known to act on these proteins and inhibit bacterial infections by disrupting the cell-wall biogenesis pathway. Penicillin-Binding proteins have recently gained importance with the increase in the number of multi-drug resistant bacteria. In this work, we have collected a dataset of over 700 Penicillin-Binding and non-Penicillin Binding Proteins and extracted various sequence-related features. We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest. We obtain a good classification performance for both the models using both the methods.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Anticipation of direction and time of perturbation modulates the onset latency of trunk muscle responses during sitting perturbations

Trunk muscles are responsible for maintaining trunk stability during sitting. However, the effects of anticipation of perturbation on trunk muscle responses are not well understood. The objectives of this study were to identify the responses of trunk muscles to sudden support surface translations and quantify the effects of anticipation of direction and time of perturbation on the trunk neuromuscular responses. Twelve able-bodied individuals participated in the study. Participants were seated on a kneeling chair and support surface translations were applied in the forward and backward directions with and without direction and time of perturbation cues. The trunk started moving on average approximately 40 ms after the perturbation. During unanticipated perturbations, average latencies of the trunk muscle contractions were in the range between 103.4 and 117.4 ms. When participants anticipated the perturbations, trunk muscle latencies were reduced by 16.8 ± 10.0 ms and the time it took the trunk to reach maximum velocity was also reduced, suggesting a biomechanical advantage caused by faster muscle responses. These results suggested that trunk muscles have medium latency responses and use reflexive mechanisms. Moreover, anticipation of perturbation decreased trunk muscles latencies, suggesting that the central nervous system modulated readiness of the trunk based on anticipatory information.     

Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R = 0.81 and the mean absolute error (MAE) = 1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.