3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

Sequence describes the primary structure of a protein, which contains important structural, characteristic, and genetic information and thereby motivates many sequence-based computational approaches to infer protein function. Among them, feature-base approaches attract increased attention because they make prediction from a set of transformed and more biologically meaningful sequence features. However, original features extracted from sequence are usually of high dimensionality and often compromised by irrelevant patterns, therefore dimension reduction is necessary prior to classification for efficient and effective protein function prediction. A protein usually performs several different functions within an organism, which makes protein function prediction a multi-label classification problem. In machine learning, multi-label classification deals with problems where each object may belong to more than one class. As a well-known feature reduction method, linear discriminant analysis (LDA) has been successfully applied in many practical applications. It, however, by nature is designed for single-label classification, in which each object can belong to exactly one class. Because directly applying LDA in multi-label classification causes ambiguity when computing scatters matrices, we apply a new Multi-label Linear Discriminant Analysis (MLDA) approach to address this problem and meanwhile preserve powerful classification capability inherited from classical LDA. We further extend MLDA by l-normalization to overcome the problem of over-counting data points with multiple labels. In addition, we incorporate biological network data using Laplacian embedding into our method, and assess the reliability of predicted putative functions. Extensive empirical evaluations demonstrate promising results of our methods.

Background The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain. Methods Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics. Results Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres. Conclusions The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.

in this paper, near infrared spectroscopy techniques are used for the detection of meat in the process of corruption time, studied the feasibility of pork freshness level. And the qualitative analysis model is established based on the software OPUS. During the model establishment process, the kinds of the class of TVBN values are re-divided 5 from 3 using the SOM network clustering to better reflect level of freshness of meat. And to increase the accuracy of prognostication, the principal component analysis is used to reduce dimension except choosing the pretreatment method of the 13-point first derivative smoothing, and the result is that the rate of correct promote and the number of which of bias of predictive class is decreased.

<abstract> <p>Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.</p> </abstract>

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Drug addiction can cause abnormal brain activation changes, which are the root cause of drug craving and brain function errors. This study enrolled drug abusers to determine the effects of different drugs on brain activation. A functional near-infrared spectroscopy (fNIRS) device was used for the research. This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. We collected the fNIRS data of 30 drug users, including 10 who used heroin, 10 who used Methamphetamine, and 10 who used mixed drugs. First, using Statistical Analysis, the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin, Methamphetamine, and mixed drugs, including Left/Right-Dorsolateral prefrontal cortex (L/R-DLPFC), Left/Right-Ventrolateral prefrontal cortex (L/R-VLPFC), Left/Right-Frontopolar prefrontal cortex (L/R-FPC), and Left/Right Orbitofrontal Cortex (L/R-OFC). Second, referencing the degrees of activation of oxyhaemoglobin concentration (HbO[Formula: see text], the study made an analysis and got the specific activation patterns of each group of the addicts. Finally, after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts, and which had the same channel numbers, the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks (CNNs). The average three-class accuracy is 67.13%. It is of great significance for the analysis of brain function errors and personalized rehabilitation.