Training Sample Set Research Articles

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance.

Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher.

Crowd localization is to predict each instance head position in crowd scenarios. Since the distance of pedestrians being to the camera are variant, there exists tremendous gaps among scales of instances within an image, which is called the intrinsic scale shift. The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic. To this end, the paper concentrates on access to tackle the chaos of the scale distribution incurred by intrinsic scale shift.We propose Gaussian Mixture Scope (GMS) to regularize the chaotic scale distribution. Concretely, the GMS utilizes a Gaussian mixture distribution to adapt to scale distribution and decouples the mixture model into sub-normal distributions to regularize the chaos within the sub-distributions. Then, an alignment is introduced to regularize the chaos among sub-distributions. However, despite that GMS is effective in regularizing the data distribution, it amounts to dislodging the hard samples in training set, which incurs overfitting. We assert that it is blamed on the block of transferring the latent knowledge exploited by GMS from data to model. Therefore, a Scoped Teacher playing a role of bridge in knowledge transform is proposed. What' s more, the consistency regularization is also introduced to implement knowledge transform. To that effect, the further constraints are deployed on Scoped Teacher to derive feature consistence between teacher and student end. With proposed GMS and Scoped Teacher implemented on four mainstream datasets of crowd localization, the extensive experiments demonstrate the superiority of our work. Moreover, comparing with existing crowd locators, our work achieves state-of-the-art via F1-measure comprehensively on four datasets.

Zero-shot learning via visual-semantic aligned autoencoder.

Zero-shot learning recognizes the unseen samples via the model learned from the seen class samples and semantic features. Due to the lack of information of unseen class samples in the training set, some researchers have proposed the method of generating unseen class samples by using generative models. However, the generated model is trained with the training set samples first, and then the unseen class samples are generated, which results in the features of the unseen class samples tending to be biased toward the seen class and may produce large deviations from the real unseen class samples. To tackle this problem, we use the autoencoder method to generate the unseen class samples and combine the semantic features of the unseen classes with the proposed new sample features to construct the loss function. The proposed method is validated on three datasets and showed good results.

Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection With an Isolation Forest-Guided Unsupervised Detector

Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a massive amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, a Gaussian statistical model is designed to remove the bands corrupted by severe noise. Then, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Next, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches. We will make HOSD and our developed library for oil spill detection publicly available at https://github.com/PuhongDuan/HOSD to further promote this research topic.

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Gesture fingertip detection plays a vital role in human-computer interaction applications such as VR and robot control. To improve the accuracy of fingertip detection in complex background, this paper proposes a fingertip detection algorithm based on a novel maximum discrimination HOG feature. Firstly, the Holistically-nested edge detection algorithm is used to detect the edge of hand images and non-hand images, and the HOG features of the contour edge are extracted to obtain the training set of positive and negative samples to reduce the influence of illumination, color, and texture on fingertip detection. Secondly, the maximum discrimination features are filtered from the positive sample set by a custom maximum discrimination feature filter and stored as a feature dictionary. The filtered maximum discrimination features and negative sample set are input into the XGBoost classifier for training and the voting rights classifier is obtained. Filtering maximum discrimination features can reduce the interference of irrelevant features and improve the performance of the classifier. Then, the KNN algorithm is used to find the best match in the dictionary, and the final fingertip position is obtained by the Meanshift algorithm. Finally, the algorithm in this paper is tested. The test results show that the accuracy of fingertip detection in this paper can reach 99 %, and the detected RMSE is kept in the range of 5 pixels, which is higher than that based on YCbCr skin color segmentation, YOLO target detection, and YOLO-YCbCr.

Serum metabolomics analysis in patients with alcohol dependence.

Alcohol dependence (AD) is a chronic recurrent mental disease caused by long-term drinking. It is one of the most prevalent public health problems. However, AD diagnosis lacks objective biomarkers. This study was aimed to shed some light on potential biomarkers of AD patients by investigating the serum metabolomics profiles of AD patients and the controls. Liquid chromatography-mass spectrometry (LC-MS) was used to detect the serum metabolites of 29 AD patients (AD) and 28 controls. Six samples were set aside as the validation set (Control: n = 3; AD group: n = 3), and the remaining were used as the training set (Control: n = 26; AD group: n = 25). Principal component analysis (PCA) and partial least squares discriminant analysis (PCA-DA) were performed to analyze the training set samples. The metabolic pathways were analyzed using the MetPA database. The signal pathways with pathway impact >0.2, value of p <0.05, and FDR < 0.05 were selected. From the screened pathways, the metabolites whose levels changed by at least 3-fold were screened. The metabolites with no numerical overlap in their concentrations in the AD and the control groups were screened out and verified with the validation set. The serum metabolomic profiles of the control and the AD groups were significantly different. We identified six significantly altered metabolic signal pathways, including protein digestion and absorption; alanine, aspartate, and glutamate metabolism; arginine biosynthesis; linoleic acid metabolism; butanoate metabolism; and GABAergic synapse. In these six signal pathways, the levels of 28 metabolites were found to be significantly altered. Of these, the alterations of 11 metabolites changed by at least 3-fold compared to the control group. Of these 11 metabolites, those with no numerical overlap in their concentrations between the AD and the control groups were GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid and L-glutamine. The metabolite profile of the AD group was significantly different from that of the control group. GABA, 4-hydroxybutanoic acid, L-glutamic acid, citric acid, and L-glutamine could be used as potential diagnostic markers for AD.

Determination of Diclofenac Diethylamine Levels in Emulgel Preparations Using NIR Spectroscopy Combined with Chemometrics

Diclofenac is an NSAID-class drug with activity as an analgesic and anti-inflammatory recommended for treating various acute and chronic pain conditions. One of the topical preparations of diclofenac that is often used is emulgel. In this study, diclofenac diethylamine levels in emulgel preparations were determined using NIR (Near-Infrared) spectroscopy and chemometric methods. Simulation samples were prepared and divided into 24 training sets and 9 test set samples. NIR spectra of training set samples were correlated with the concentration of diclofenac diethylamine using partial least squares (PLS), principal component regression (PCR), and support vector regression (SVR). The best model was validated using leave one out cross validation (LOOCV) and external validation using test set samples. The comparison method used in this study was the validated TLC Densitometry method. The best calibration model was PLS, with an R2 value of 0.990 and RMSE of 0.171. The results of R2 and RMSE of LOOCV were 0.989 up to 0.990 and 0.167 up to 0.178, respectively. The result of R2 and RMSEP external validation were 0.991 and 0.146, respectively. The precision and accuracy method showed RSD of 3.37% and a % recovery of 99.78%. The results of determining the sample levels obtained from NIR and TLC Densitometry methods tested with the Two-Paired Sample T Test and showed that the two methods have no significant differences with a significance value of more than 0.05.

Photo-Fenton Degradation Process of Styrene in Nitrogen-Sealed Storage Tank.

Using styrene as a proxy for VOCs, a new method was developed to remove styrene gas in nitrogen atmospheres. The effect on the styrene removal efficiency was explored by varying parameters within the continuum dynamic experimental setup, such as ferrous ion concentration, hydrogen peroxide concentration, and pH values. The by-products are quantized by a TOC analyzer. The optimal process conditions were hydrogen peroxide at 20 mmol/L, ferrous ions at 0.3 mmol/L and pH 3, resulting in an average styrene removal efficiency of 96.23%. In addition, in this study, we construct a BAS-BP neural network model with experimental data as a sample training set, which boosts the goodness-of-fit of the BP neural network and is able to tentatively predict styrene gas residuals for different front-end conditions.

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.

Three-Branch Random Forest Intrusion Detection Model

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at the current problems, considering increasing the probability of essential features being selected, a network intrusion detection model based on three-way selected random forest (IDTSRF) is proposed, which integrates three decision branches and random forest. Firstly, according to the characteristics of attributes, it is proposed to evaluate the importance of attributes by combining decision boundary entropy, and using three decision rules to divide attributes; secondly, to keep the randomness of attributes, three attribute random selection rules based on attribute randomness are established, and a certain number of attributes are randomly selected from three candidate fields according to conditions; finally, the training sample set is formed by using autonomous sampling method to select samples and combining three randomly selected attribute sets randomly, and multiple decision trees are trained to form a random forest. The experimental results show that the model has high precision and recall.

Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction.

Patients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models. A prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models' predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM). The incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85-0.95) and GBC (AUC: 0.91, 95% CI: 0.86-0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79-0.91) and MLPC (AUC: 0.85, 95% CI: 0.78-0.92). SVC (AUC: 0.90, 95% CI: 0.85-0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models' 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor variables. GAM analysis demonstrated a linear and non-linear significant interaction between NIHSS and CMB and between NIHSS and infarction size, respectively. Cerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.

A Multi-Cohort Gene Expression Classifier (ALLCatchR) Identifies B-Precursor ALL Subtypes and Their Developmental Trajectories across Age Groups

The molecular landscape of B cell precursor acute lymphoblastic leukemia (BCP-ALL) describes >20 disease subtypes defined by initiating genomic alterations and corresponding gene expression signatures. Some subtypes represent phenocopies with shared gene expression signatures but distinct or absent genomic drivers. The majority of BCP-ALL subtypes have been acknowledged as diagnostic entities by the currently revised classifications of hematologic malignancies (WHO-HAEM5 / International Consensus Classification). Thus, unified diagnostic approaches are needed to systematically identify subtypes in individual BCP-ALL cases in clinical routine. Gene expression signatures represent the shared functional equivalent of heterogeneous genomic driver alterations. Therefore, we established a gene expression-based classifier to identify BCP-ALL subtypes and related clinical and biological phenotypes. We analyzed a total of n=3477 BCP-ALL gene expression profiles obtained from transcriptome sequencing of adult (n=1253) and pediatric patients (n=2224) including publicly available (n=2074; Gu Z et al. Nat. Genet. 2019; Schmidt B. et al. Blood Adv. 2022) and own data sets (n=1403) from adult and pediatric European study groups (GMALL, AIEOP-BFM, CELL). Raw read counts from 6 cohorts representing heterogeneous library preps (i.e., stranded/unstranded; mRNA/total RNA), sequencing platforms/depths, and gene count quantifications were included. All samples had been previously allocated to molecular BCP-ALL subtypes based on integration of independent genomic profiling, gene fusion calling and gene expression. For classification, data with defined subgroup allocation were randomly split into training (n=1831; 70%) and testing sets (n=785; 30%) and two hold-out studies (n=305). To define gene expression signatures of 21 BCP-ALL subtypes, we used training set samples and extracted specific gene sets employing different feature selection algorithms as well as ranking of genes in individual subtypes. Based on these definitions we established a BCP-ALL subtype classifier (ALLCatchR), which integrates machine learning models (support vector machines) and nearest-neighbors associations of single sample gene set enrichment analyses (Fig. A). ALLCatchR performed molecular subtype allocation for the testing set at high sensitivity (0.934±0.165) and specificity (0.997±0.005) on average across subtypes. A similar performance was achieved in the two hold-out cohorts despite completely independent data structures which have not been shown to the classifier (sensitivity: 0.946±0.097 / specificity: 0.996±0.007). Overall accuracies of 0.952 and 0.92 for testing and hold-out-data sets indicate that ALLCatchR reliably identifies molecular BCP-ALL subtypes in mixed adult / pediatric cohorts based on gene expression profiles alone (Fig. A, below). Among n=44/1090 (4%) samples of the combined hold-out/testing set with ambiguous subtype allocation (deemed 'unclassified'), ALLCatchR provides candidate subtype allocations (sensitivity: 0.671±0.393 / specificity: 0.978±0.036) for confirmation based on genomic driver alterations. In addition, we modified our classifier to predict baseline variables such as patient's sex (accuracy: 0.991), immunophenotype (accuracy: 0.935) or blast count (R=0.633). To define the corresponding B cell differentiation stage of origin, we compared BCP-ALL subtypes to differentiation stage-specific gene sets extracted from 7 FACS-sorted lymphopoietic subsets from healthy bone marrow donors (n=4). We observed defined BCP-ALL subtype-specific enrichment patterns which can be grouped along 4 major differentiation stages (Pro-B to Pre-B-II-Large, Fig. 1B) with a high degree of similarity in independent adult and pediatric cohorts. Thus, B cell developmental trajectories underlying BCP-ALL subtypes are dissected at high resolution by gene expression profiling and differentiation stages may favor the selection of specific leukemogenic drivers. ALLCatchR is a gene expression classifier for 21 established BCP-ALL molecular subtypes, which performs at independently validated cohorts with an accuracy of >90% and allows imputation of clinical baseline variables as well as underlying B cell developmental trajectories. It provides a reliable basis for systematic diagnostic approaches in pediatric and adult BCP-ALL patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Prediction of Elephant Movement Using Intellectual Virtual Fencing Model

The capability to navigate and orientate is crucially a significant factor to determine the survival of all migratory animals like elephants. The sustainability of animals is constrained with a limited amount of techniques available for analysis of complex animals’ behavioral responses. Various approaches are used to track animals’ movement like elephants crossing the railway track, roads have to be varied based on the degree of accuracy that is needed essentially. However, the existing approaches fail to trigger an alert in some cases. To overcome these limitations, a novel Intellectual Inertial Measurement Unit (IIMU) is proposed where the data are acquired from the aerial elephant dataset with a set of training and testing image samples. Data collected with these dataset are analyzed for triggering Virtual Fencing (VF) and to alert animals to avoid danger. This work attempts to validate that this IIMU installed with animals’ bodies can be used to evaluate patterns related to the animal’s movement. The collected data are provided for filtering using Levenberg Marquardt Algorithm to reduce the noise over the data and to enhance the prediction accuracy. The pattern set undergoes training with Artificial Neural Network (ANN) and optimized with Elephant Optimization to evaluate the prediction accuracy. Based on the evaluation, the model shows better prediction accuracy in case of emergency and alert is triggered to save the life of elephants. Here, some performance metrics like accuracy, precision, F-measure, recall, ROC are evaluated to show the significance of EPO-ANN model. The model outperforms the existing standard SVM model and gives higher prediction accuracy.

Incremental Learning of Human Activities in Smart Homes.

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease.

Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD. Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers. Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs. Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

An intelligent fault diagnosis scheme for hydropower units based on the pattern recognition of axis orbits

Fault diagnosis plays an important role in maintaining the safe and stable operation of hydropower units. This paper presents an intelligent fault diagnosis scheme for hydropower units based on the pattern recognition of axis orbits. Firstl the vibration signals in the X and Y directions that constitute the axis orbit of the unit shaft are processed by the denoising method based on modal decomposition and permutation entropy. Second, the relative position and distribution of the axis orbits for different samples in the image window are unified. Third, the trained convolution neural network is chosen as the classifier to recognize the axis orbit image for fault-type recognition. Through the analysis of the measured data of a hydropower station, the influence of the sample number of the training set and the size of the axis orbit image on the performance of the proposed method and the necessity of denoising operation are studied. Compared with the existing methods, the proposed method has higher fault recognition accuracy and better generalization performance for different training sample sets. The results indicate that the proposed method is an effective alternative for the fault diagnosis of hydropower units.

Classification of natural images inspired by the human visual system

In this paper, a three-step model based on the integration of Deep Neural Networks (DNN) and Decision Models is introduced for image classification which is inspired by the human visual system. To make a decision about an object, many actions should be done in a hierarchical process in the brain. First, the retina receives visual stimuli and transfers them to the visual cortex in the brain. The information extracted in the visual cortex, is accumulated over time to select an appropriate response. Many of the current decision-making models do not show how each image is converted into useful information for the decision model. Some models have used neural networks to convert each image into the information needed in the decision-making model; however, the role of the retina is ignored among these models. In this paper, a combination of retina inspired filters, CNN-based description and accumulator-based decision model is used to classify images. This model’s structure resembles the human brain due to the usage of the DoG filter bank as retina inspired filter in the first stage of it. This model shows a significant improvement in accuracy in comparison to other models; furthermore, its performance is acceptable even with the small sample training set.

Towards localizing head shots – Forensic sub-differentiation of anatomical brain regions by differential RNA expression

The detection of organ tissues, including brain, in even challenged forensic sample material by forensic RNA analysis has become a valid and routinely used method of trace contextualization. However, in cases of severe head traumas dealt by blunt impacts or firearm projectiles, for example in situations when multiple offenders handled different weapons, it can be beneficial for criminal investigations to determine by analysis of traces of biological material which area of the head had been hit (and by whom) by proxy of detecting which part of the brain had been injured. A whole transcriptome analysis employing massive parallel RNA Sequencing of four different anatomical areas of the cerebral cortex (frontal, temporal, parietal, occipital) was performed on a training set of autopsy samples from brain tissue. Applying different bioinformatic classification algorithms brain area specific mRNA candidates were selected and suitable markers were then validated on an independent test set of autopsy brain samples using quantitative RT-PCR. Here we found, that the differences in mRNA transcription of these two markers between the four brain regions were too small and less pronounced to be useful for and applicable in forensic RNA analysis. Hence, we conclude, that sub-differentiation of these brain regions via RNA expression analysis is out of forensic scope.

Serologic Biomarkers of Progression Toward Diagnosis of Rheumatoid Arthritis in Active Component Military Personnel.

To identify a panel of serum biomarkers that could specifically identify imminent cases of rheumatoid arthritis (RA) before diagnosis. Serum samples were collected at 4 time points from active component US military personnel, including 157 anti-citrullinated protein antibody (ACPA)-seropositive and 50 ACPA-seronegative RA subjects, 100 reactive arthritis (ReA) subjects, and 76 healthy controls. The cohorts were split into 2 phases, with samples tested on independent proteomic platforms for each phase. Classification models of RA diagnosis based on samples obtained within 6 months prior to diagnosis were developed both in univariate analyses and by multivariate random forest modeling of training sample sets and testing sample sets from each phase. Increases in serum analytes, including C-reactive protein levels, serum amyloid A, and soluble programmed cell death 1 (PD-1), were observed in seropositive RA subjects at the time point closest to diagnosis, up to several years before diagnosis. Only a small fraction of RA subjects had levels above the 95th percentile of healthy control levels until the time period within 6 months of diagnosis. For classification of RA diagnosis using samples obtained within 6 months prior to diagnosis, soluble PD-1 provided superior specificity compared to ReA cases (>89%), with a sensitivity of 48% for RA classification. An 8-analyte model provided superior sensitivity (69%), with comparable specificity relative to ReA (>82%). Our findings demonstrate that imminent RA diagnosis could be classified with high specificity, relative to healthy controls and ReA cases, using a panel of cytokines measured in serum samples collected within 6 months before actual diagnosis.

Comparison and optimization of rapid and nondestructive qualitative analysis models based on fourier transform infrared spectroscopy information of drugs

It is a key issue in the field of drug prevention and control to realize the rapid and nondestructive qualitative analysis of drugs. In this study, spectral data of 196 pieces of drug samples based on actual cases which are three types in total, were sorted out. The Bayesian discriminant analysis (BDA) models were constructed based on full spectral, fingerprint spectral and functional group spectral data sets were extracted by principal component analysis (PCA). The classification results for different types of data sets were compared, and the optimal spectral region were selected. In the BDA, the model based on the data of the fingerprint spectra the highest classification accuracy, and the classification accuracy of the sample training set based on three types of drugs was 0.837. The identification models based on data sets of fingerprint spectra were established by multilayer perceptron (MLP) and support vector machines (SVM), as well as compare the model parameters of SVM models based on different kernel functions. According to the results, it shows that in the three types of drugs recognition and classification process, the SVM model based on linear kernel functions has achieved the best classification effect, with the overall classification accuracy of the sample training set reaches 0.849, and the model can fully classify drugs of the same type. The classification results were ideal for the SVM model based on linear kernel functions for fingerprint spectral data set. The results of this study demonstrate the potential of Fourier transform infrared spectroscopy in combination with the chemometrics methods of SVM as a new method for the identification of different types and different kinds of drugs of the same type.