Single-molecule correlated chemical probing of RNA.

Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify through-space interactions, create 3D models for RNAs spanning 80-265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA.

Role of the line probe assay INNO-LiPA HBV DR and ultradeep pyrosequencing in detecting resistance mutations to nucleoside/nucleotide analogues in viral samples isolated from chronic hepatitis B patients.

Despite the effectiveness of nucleoside/nucleotide analogues in the treatment of chronic hepatitis B (CHB), their long-term administration is associated with the emergence of resistant hepatitis B virus (HBV) mutants. In this study, mutations resulting in antiviral resistance in HBV DNA samples isolated from 23 CHB patients (nine treatment naïve and 14 treated previously) were studied using a line probe assay (INNO-LiPA HBV DR; Innogenetics) and ultradeep pyrosequencing (UDPS) methods. Whilst the INNO-LiPA HBV DR showed no resistance mutations in HBV DNA samples from treatment-naive patients, mutations mediating lamivudine resistance were detected in three samples by UDPS. Among patients who were treated previously, 19 mutations were detected in eight samples using the INNO-LiPA HBV DR and 29 mutations were detected in 12 samples using UDPS. All mutations detected by the INNO-LiPA HBV DR were also detected by UDPS. There were no mutations that could be detected by INNO-LiPA HBV DR but not by UDPS. A total of ten mutations were detected by UDPS but not by INNO-LiPA HBV DR, and the mean frequency of these mutations was 14.7 %. It was concluded that, although INNO-LiPA HBV DR is a sensitive and practical method commonly used for the detection of resistance mutations in HBV infection, UDPS may significantly increase the detection rate of genotypic resistance in HBV at an early stage.

Application of canonical correlation analysis for identifying viral integration preferences.

MOTIVATION: Gene therapy aims at using viral vectors for attaching helpful genetic code to target genes. Therefore, it is of great importance to develop methods that can discover significant patterns around viral integration sites. Canonical correlation analysis is an unsupervised statistical tool that is used to describe the relations between two related views of the same semantic object, which fits well for identifying such salient patterns. RESULTS: Proposed method is demonstrated on a sequence dataset obtained from a study on HIV-1 preferred integration regions. The subsequences on the left and right sides of the integration points are given to the method as the two views, and statistically significant relations are found between sequence-driven features derived from these two views, which suggest that the viral preference must be the factor responsible for this correlation. We found that there are significant correlations at x=5 indicating a palindromic behavior surrounding the viral integration site, which complies with the previously reported results. AVAILABILITY: Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.

Wearable Sensors Detect Movement Differences in the Portable Warrior Test of Tactical Agility After mTBI in Service Members.

INTRODUCTION: Assessment of functional recovery of service members following a concussion is central to their return to duty. Practical military-relevant performance-based tests are needed for identifying those who might need specialized rehabilitation, for evaluating the progress of recovery, and for making return-to-duty determinations. One such recently developed test is the 'Portable Warrior Test of Tactical Agility' (POWAR-TOTAL) assessment designed for use following concussion in an active duty population. This agility task involves maneuvers used in military training, such as rapid stand-to-prone and prone-to-stand transitions, combat rolls, and forward and backward running. The effect of concussion on the performance of such maneuvers has not been established. MATERIALS AND METHODS: The Institutional Review Board-approved study was conducted at Ft. Bragg, North Carolina, on 57 healthy control (HC) service members (SMs) and 42 well-matched SMs who were diagnosed with concussion and were referred for physical therapy with the intent to return to duty. Each study participant performed five consecutive trials of the POWAR-TOTAL task at full exertion while wearing inertial sensors, which were used to identify the constituent task maneuvers, or phases, and measure their durations. Statistical analyses were performed on durations of three main phases: (1) rising from prone and running, (2) lowering from vertical to prone, and (3) combat rolls. RESULTS: None of the three phases showed significant correlation with age (range 18-45 years) in either group. Gradual improvement in all three phase durations across five trials was observed in the HC group, but not in the concussed group. On average, control subjects performed significantly faster (P < .004 or less) than concussed subjects in all trials in the lowering and rolling phases, but less so in the rising/running phase. Membership in the concussed group had a strong effect on the lowering phase (Cohen's d = 1.05), medium effect on the rolling phase (d = 0.72), and small effect on the rising/running phase (d = 0.49). Individuals in the HC group who had a history of prior concussions were intermediate between the concussed group and the never-concussed group in the lowering and rolling phases. Duration of transitional movements (lowering from standing to prone and combat rolls) was better at differentiating individuals' performance by group (receiver operating characteristic area under the curve [AUC] = 0.83) than the duration of the entire POWAR-TOTAL task (AUC = 0.71). CONCLUSIONS: Inertial sensor analysis reveals that rapid transitional movements (such as lowering from vertical to prone position and combat rolls) are particularly discriminative between SMs recovering from concussion and their concussion-free peers. This analysis supports the validity of POWAR-TOTAL as a useful tool for therapists who serve military SMs.

A novel partial sequence alignment tool for finding large deletions.

Finding large deletions in genome sequences has become increasingly more useful in bioinformatics, such as in clinical research and diagnosis. Although there are a number of publically available next generation sequencing mapping and sequence alignment programs, these software packages do not correctly align fragments containing deletions larger than one kb. We present a fast alignment software package, BinaryPartialAlign, that can be used by wet lab scientists to find long structural variations in their experiments. For BinaryPartialAlign, we make use of the Smith-Waterman (SW) algorithm with a binary-search-based approach for alignment with large gaps that we called partial alignment. BinaryPartialAlign implementation is compared with other straight-forward applications of SW. Simulation results on mtDNA fragments demonstrate the effectiveness (runtime and accuracy) of the proposed method.

Neocortical layer 4 as a pluripotent function linearizer.

A highly effective kernel-based strategy used in machine learning is to transform the input space into a new "feature" space where nonlinear problems become linear and more readily solvable with efficient linear techniques. We propose that a similar "problem-linearization" strategy is used by the neocortical input layer 4 to reduce the difficulty of learning nonlinear relations between the afferent inputs to a cortical column and its to-be-learned upper layer outputs. The key to this strategy is the presence of broadly tuned feed-forward inhibition in layer 4: it turns local layer 4 domains into functional analogs of radial basis function networks, which are known for their universal function approximation capabilities. With the use of a computational model of layer 4 with feed-forward inhibition and Hebbian afferent connections, self-organized on natural images to closely match structural and functional properties of layer 4 of the cat primary visual cortex, we show that such layer-4-like networks have a strong intrinsic tendency to perform input transforms that automatically linearize a broad repertoire of potential nonlinear functions over the afferent inputs. This capacity for pluripotent function linearization, which is highly robust to variations in network parameters, suggests that layer 4 might contribute importantly to sensory information processing as a pluripotent function linearizer, performing such a transform of afferent inputs to a cortical column that makes it possible for neurons in the upper layers of the column to learn and perform their complex functions using primarily linear operations.

Organizational commitment of military physicians.

An individual's loyalty or bond to his or her employing organization, referred to as organizational commitment, influences various organizational outcomes such as employee motivation, job satisfaction, performance, accomplishment of organizational goals, employee turnover, and absenteeism. Therefore, as in other sectors, employee commitment is crucial also in the healthcare market. This study investigates the effects of organizational factors and personal characteristics on organizational commitment of military physicians using structural equation modeling (SEM) on a self-report, cross-sectional survey that consisted of 635 physicians working in the 2 biggest military hospitals in Turkey. The results of this study indicate that professional commitment and organizational incentives contribute positively to organizational commitment, whereas conflict with organizational goals makes a significantly negative contribution to it. These results might help develop strategies to increase employee commitment, especially in healthcare organizations, because job-related factors have been found to possess greater impact on organizational commitment than personal characteristics.

Quantification of Mild Traumatic Brain Injury via Cortical Metrics: Analytical Methods.

Mild traumatic brain injuries are difficult to diagnose or assess with commonly used diagnostic methods. However, the functional state of cerebral cortical networks can be rapidly and effectively probed by measuring tactile-based sensory percepts (called cortical metrics), which are designed to exercise various components of cortical machinery. In this study, such cortical metrics were obtained from 52 college students before and after they experienced sports-related concussions by delivering vibrotactile stimuli to the index and middle fingertips. Performance on four of the sensory test protocols is described: reaction time, amplitude discrimination, temporal order judgment, and duration discrimination. The collected test performance data were analyzed using methods of uni- and multivariate statistics, receiver operated characteristic (ROC) curves, and discriminant analysis. While individual cortical metrics vary extensively in their ability to discriminate between control and concussed subjects, their combined discriminative performance greatly exceeds that of any individual metric, achieving cross-validated 93.0% sensitivity, 92.3% specificity, 93.0% positive predictive value, and 92.3% negative predictive value. The cortical metrics vector can be used to track an individual's recovery from concussion. The study thus establishes that cortical metrics can be used effectively as a quantitative indicator of central nervous system health status.

Discriminative Feature Extraction by a Neural Implementation of Canonical Correlation Analysis.

The canonical correlation analysis (CCA) aims at measuring linear relationships between two sets of variables (views) that can be used for feature extraction in classification problems with multiview data. However, the correlated features extracted by the CCA may not be class discriminative, since CCA does not utilize the class labels in its traditional formulation. Although there is a method called discriminative CCA (DCCA) that aims to increase the discriminative ability of CCA inspired from the linear discriminant analysis (LDA), it has been shown that the extracted features with this method are identical to those by the LDA with respect to an orthogonal transformation. Therefore, DCCA is simply equivalent to applying single-view (regular) LDA to each one of the views separately. Besides, DCCA and the other similar DCCA approaches have generalization problems due to the sample covariance matrices used in their computation, which are sensitive to outliers and noisy samples. In this paper, we propose a method, called discriminative alternating regression (D-AR), to explore correlated and also discriminative features. D-AR utilizes two (alternating) multilayer perceptrons, each with a linear hidden layer, learning to predict both the class labels and the outputs of each other. We show that the features found by D-AR on training sets significantly accomplish higher classification accuracies on test sets of facial expression recognition, object recognition, and image retrieval experimental data sets.

Path-based connectivity for clustering genome sequences.

Clustering is an unsupervised data mining tool and in bioinformatics, clustering genome sequences is used to group related biological sequences when there is no additional supervision. Sequence clusters are often related with gene/protein families, which can shed some light onto determining tertiary structures. To extract such hidden and valuable structures in a data set of genome sequences can benefit from better clustering methods such as the recently popular Spectral Clustering. In this study, we apply spectral clustering and its improved variations to sequence clustering task in our efforts to develop a novel approach for improving it.

Analysis of shared miRNAs of different species using ensemble CCA and genetic distance.

MicroRNA is a type of single stranded RNA molecule and has an important role for gene expression. Although there have been a number of computational methodologies in bioinformatics research for miRNA classification and target prediction tasks, analysis of shared miRNAs among different species has not yet been addressed. In this article, we analyzed miRNAs that have the same name and function but have different sequences and belong to different (but closely related) species which are constructed from the online miRBase database. We used sequence-driven features and performed the standard and the ensemble versions of Canonical Correlation Analysis (CCA). However, due to its sensitivity to noise and outliers, we extended it using an ensemble approach. Using linear combinations of dimer features, the proposed Ensemble CCA (ECCA) method has identified higher test-set-correlations than CCA. Moreover, our analysis reveals that the Redundancy Index of ECCA applied to a pair of species has correlation with their genetic distance.

Combining multiple clusterings for protein structure prediction.

Computational annotation and prediction of protein structure is very important in the post-genome era due to existence of many different proteins, most of which are yet to be verified. Mutual information based feature selection methods can be used in selecting such minimal yet predictive subsets of features. However, as protein features are organised into natural partitions, individual feature selection that ignores the presence of these views, dismantles them, and treats their variables intermixed along with those of others at best results in a complex un-interpretable predictive system for such multi-view datasets. In this paper, instead of selecting a subset of individual features, each feature subset is passed through a clustering step so that it is represented in discrete form using the cluster indices; this makes mutual information based methods applicable to view-selection. We present our experimental results on a multi-view protein dataset that are used to predict protein structure.

Mitochondrial DNA profiling via genomic analysis in mesial temporal lobe epilepsy patients with hippocampal sclerosis.

INTRODUCTION: Mitochondria have an essential role in neuronal excitability and neuronal survival. In addition to energy production, mitochondria also play a crucial role in the maintenance of intracellular calcium homeostasis, generation of reactive oxygen species and mechanisms of cell death. There is a relative paucity of data about the role of mitochondria in epilepsy. Mitochondrial genome analysis is rarely carried out in the investigation of some diseases. In mesial temporal lobe epilepsies (MTLE) cases, genome analysis has never been used previously. The aim of this study is to show mitochondrial dysfunctions using genome analysis in patients with MTLE-hippocampal sclerosis (HS). METHODS: 44 patients with MTLE-HS and 86 matched healthy unrelated controls were included in this study. The patients were divided into four groups according to their clinical presentation as the following: Group 1 consists of patients with intractable epilepsy who refused operation; Group 2 of operated seizure free patients; Group 3 of operated patients with seizures; and Group 4 unoperated seizure free patients with or without antiepileptic drugs. Blood samples were used to isolate DNA. Parallel tagged sequencing was employed to allow pyrosequencing of 130 samples. Complete mtDNA is amplified in two overlapping fragments (11 and 9 kb). The PCR amplicons were pooled in equimolar ratios. Titanium kits were used to produce shotgun libraries according to the manufacturer's protocol. RESULTS: The average coverage in total was 130 ± 30 and an average of 2365127 bases and 337 bp fragment length was received from all samples. The mean mtDNA heteroplasmy in patients was 26.35 ± 12.3 and in controls 25.03 ± 9.34. Three mutations had prominently high significance in patient samples. The most significantly associated variation was located in the MT-ATP-8 gene (8502 A>T, Asn46Ile) whereas the other two were in the MT-ND4 (11994 C>T, Thr412Ile) and MT-ND5 (13231 A>C, Lys299Gln) genes. CONCLUSIONS: We have observed that three mutations were significantly related to the presence of epilepsy. These mutations were found at the 8502, 11994, and 13,231 bp of mtDNA, which resulted in amino acid changes at the MT-ATP-8, MT-ND4 and MT-ND5 genes. Finding mutations can lead us to knowing more about the pathophysiology of the MTLE disease.

Multi objective SNP selection using pareto optimality.

Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.

Comparison of aggregators for multi-objective SNP selection.

SNPs (Single Nucleotide Polymorphisms) are genomic variants that associate with many genetic characteristics. These variants can also be utilized to track the on-going mutation in population genetics. The goal of this study was to select the most relevant SNP subsets for discriminating ethnic groups. Each SNP was evaluated by its: i) Mutual information, ii) Relief-F score, iii) Loadings of the first principal component, iv) Loadings of the second principal component. Combining these four feature ranking criteria in different ways, three different aggregation methods (Pareto Optimal, Condorcet, MC4) were compared with respect to their SNP selection accuracies. Results showed that SNP subsets chosen with Pareto Optimal yielded better classification accuracy.

Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings.

There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.

Selection of vocal features for Parkinson's Disease diagnosis.

Parkinson's Disease (PD) is a neurodegenerative motor system disorder, which also causes vocal impairments for most of its patients. A number of recent exploratory studies have evaluated the feasibility of detecting voice disorders by applying data mining tools to acoustic features extracted from speech recordings of patients. Selection of a minimal yet descriptive set of features is crucial for improving the classifier generalisation capability and interpretability of the classification model as well as for reducing the burden of data preprocessing. We propose a hybrid of feature selection and cross-validation procedures to lower the bias in the assessment of classifier accuracy.

A genome-wide analysis of lentivector integration sites using targeted sequence capture and next generation sequencing technology.

One application of next-generation sequencing (NGS) is the targeted resequencing of interested genes which has not been used in viral integration site analysis of gene therapy applications. Here, we combined targeted sequence capture array and next generation sequencing to address the whole genome profiling of viral integration sites. Human 293T and K562 cells were transduced with a HIV-1 derived vector. A custom made DNA probe sets targeted pLVTHM vector used to capture lentiviral vector/human genome junctions. The captured DNA was sequenced using GS FLX platform. Seven thousand four hundred and eighty four human genome sequences flanking the long terminal repeats (LTR) of pLVTHM fragment sequences matched with an identity of at least 98% and minimum 50 bp criteria in both cells. In total, 203 unique integration sites were identified. The integrations in both cell lines were totally distant from the CpG islands and from the transcription start sites and preferentially located in introns. A comparison between the two cell lines showed that the lentiviral-transduced DNA does not have the same preferred regions in the two different cell lines.

Telediagnosis of Parkinson's disease using measurements of dysphonia.

Parkinson's disease (PD) is a neurological illness which impairs motor skills, speech, and other functions such as mood, behavior, thinking, and sensation. It causes vocal impairment for approximately 90% of the patients. As the symptoms of PD occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Such dysphonia features extracted from the voice come in variety and most of them are interrelated. The purpose of this study is twofold: (1) to select a minimal subset of features with maximal joint relevance to the PD-score, a binary score indicating whether or not the sample belongs to a person with PD; and (2) to build a predictive model with minimal bias (i.e. to maximize the generalization of the predictions so as to perform well with unseen test examples). For these tasks, we apply the mutual information measure with the permutation test for assessing the relevance and the statistical significance of the relations between the features and the PD-score, rank the features according to the maximum-relevance-minimum-redundancy (mRMR) criterion, use a Support Vector Machine (SVM) for building a classification model and test it with a more suitable cross-validation scheme that we called leave-one-individual-out that fits with the dataset in hand better than the conventional bootstrapping or leave-one-out validation methods.

Classification of the colonic polyps in CT-colonography using region covariance as descriptor features of suspicious regions.

We present an algorithm to classify polyps in CT colonography images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, they cannot simply be fed to traditional machine learning tools such as support vector machines (SVMs) or artificial neural networks (ANNs). To benefit from the simple yet one of the most powerful nonparametric machine learning approach k-nearest neighbor classifier, it suffices to compute the pairwise distances among the covariance descriptors using a distance metric involving their generalized eigenvalues, which also follows from the Lie group structure of positive definite matrices. This approach is fast and discriminates polyps from non-polyps with high accuracy using only a small size descriptor, which consists of 36 unique features per image region extracted from the suspicious regions that we have obtained by combined cellular neural network (CNN) and template matching detection method. These suspicious regions are, in average, 15 x 17 = 255 pixels in our experiments.