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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. [ABSTRACT FROM AUTHOR]

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., (© The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Objective: Arachidonic acid (ARA) is essential for the fluidity, selective permeability, and flexibility of the cell membrane. It is an important factor for the function of all cells, particularly in the nervous system, immune system, and vascular endothelium. ARA is the second most common polyunsaturated fatty acid in the phospholipids of the nerve cell membrane after docosahexaenoic acid. ARA metabolites have many kinds of physiologic roles. The major action of ARA metabolites is the promotion of the acute inflammatory response, mediated by the production of pro-inflammatory mediators such as PGE2 and PGI2, followed by the formation of lipid mediators, which have pro-resolving effects. Another important action of ARA derivatives, especially COX, is the regulation of vascular reactivity through PGs and TXA2. There is significant involvement of ARA metabolites in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and neuropsychiatric disorders. ARA derivatives also make an important contribution to acute stroke, global ischemia, subarachnoid hemorrhage, and anticoagulation-related hemorrhagic transformation., Conclusion: In this review, we have discussed experimental and human study results of neurologic disorders related to ARA and its metabolites in line with treatment options., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Neuroinflammation has an important role in the pathophysiology of migraine, which is a complex neuro-glio-vascular disorder. The main aim of this review is to highlight findings of cortical spreading depolarization (CSD)-induced neuroinflammatory signaling in brain parenchyma from the inflammasome perspective. In addition, we discuss the limited data of the contribution of inflammasomes to other aspects of migraine pathophysiology, foremost the activation of the trigeminovascular system and thereby the generation of migraine pain., Main Body: Inflammasomes are signaling multiprotein complexes and key components of the innate immune system. Their activation causes the production of inflammatory cytokines that can stimulate trigeminal neurons and are thus relevant to the generation of migraine pain. The contribution of inflammasome activation to pain signaling has attracted considerable attention in recent years. Nucleotide-binding domain (NOD)-like receptor family pyrin domain containing 3 (NLRP3) is the best characterized inflammasome and there is emerging evidence of its role in a variety of inflammatory pain conditions, including migraine. In this review, we discuss, from an inflammasome point of view, cortical spreading depolarization (CSD)-induced neuroinflammatory signaling in brain parenchyma, the connection with genetic factors that make the brain vulnerable to CSD, and the relation of the inflammasome with diseases that are co-morbid with migraine, including stroke, epilepsy, and the possible links with COVID-19 infection., Conclusion: Neuroinflammatory pathways, specifically those involving inflammasome proteins, seem promising candidates as treatment targets, and perhaps even biomarkers, in migraine.

Continuous-flow left ventricular assist devices (LVADs) reduce peak systolic flow, increase diastolic flow, and eliminate pulsatility of circulation. Altered blood flow may lead to a change in end-organ perfusion. Analysis of the flow dynamics of the arteries of end organs, such as the brain, may indicate whether an organ is perfused sufficiently. The aim of this study is to evaluate and identify the flow pattern changes of carotid (CA) and middle cerebral arteries (MCA) in LVAD patients and to compare with heart failure patients and healthy volunteers. Eighty-nine individuals were included in this cross-sectional study. Participants were divided into three groups: LVAD patients (n = 31), heart failure patients (n = 26), and healthy volunteers (n = 27). Carotid and transcranial Doppler ultrasonography were performed for all study groups for peak systolic velocity (PSV), end-diastolic velocity (EDV), pulsatility (PI), and resistive (RI) indices of CA and MCA. Flow dynamics were compared between the groups. Doppler ultrasonographic data were analyzed at a median 12 (3-47) months after LVAD implantation. CA-PSV was lower in LVAD group compared with the other two groups (P < .001), MCA-PSV of LVAD and heart failure groups were similar and lower than healthy volunteers (P < .05). The highest values for CA-EDV were found in the LVAD group (P < .05). MCA-EDV values were found to be lowest in heart failure group (P < .05). For PI and RI, in all CA and MCA, the LVAD group had lower indices compared with the other two groups (P < .001). In addition, MCA flow analysis in patients with LVADs was identified for the first time with this study., (© 2020 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.)

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., (© Association of Military Surgeons of the United States 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

Background The pathophysiology of reversible cerebral vasoconstriction syndrome (RCVS) is not known. Published cases have documented coexisting cervical artery dissection and unruptured aneurysms, raising the possibility that ultrastructural vessel wall abnormalities underlie the development of vascular lesions as well as RCVS. Methods In this retrospective study we compared the frequency of neurovascular abnormalities in 158 consecutive RCVS patients, 44 patients with primary angiitis of the central nervous system (PACNS, positive controls), and 177 non-stroke patients with acute neurological symptoms (non-arteriopathy controls). Results Coexisting neurovascular abnormalities were significantly higher ( p < 0.001) in RCVS (23%) as compared to the PACNS (5%) or non-arteriopathy groups (8%). Cervical artery dissections were noted only in the RCVS group (8%, p < 0.001). The RCVS group had more unruptured aneurysms than PACNS (13% vs. 5%, p = 0.099) or non-arteriopathy controls (13% vs. 7%, p = 0.05). Seven RCVS patients also had other vascular malformations (venous anomaly, cavernous malformations, fibromuscular dysplasia). There was no significant association between coexisting vascular abnormalities and brain lesions or discharge clinical outcome in the RCVS group. Conclusion The high prevalence and heterogeneous anatomy of coexisting vascular lesions suggest that subtle ultrastructural arterial wall abnormalities may contribute to their development and also predispose to RCVS.

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.

The aims of this study were to measure the levels of interleukin-33 (IL-33) and soluble Suppression of Tumorigenicity 2 (sST2) in patients with newly diagnosed primary hypertension (HT) and to determine the relationship between carotid intima-media thickness (CIMT) and IL-33/sST2. Eighty-two patients with newly diagnosed primary HT and ninety healthy volunteers were included in the study. CIMT ⩾0.9 mm was considered as significant for subclinical atherosclerosis. The sST2 levels of patients with primary HT were higher than those of the control group, whereas the IL-33 levels of these patients were much lower than those of the control group. The sST2 levels were higher in patients with subclinical atherosclerosis than in control subjects or patients with primary HT but not with subclinical atherosclerosis. In the primary HT group, sST2 had a positive correlation with CIMT, 24-h systolic-diastolic blood pressure, low-density lipoprotein and C-reactive protein, whereas sST2 had a negative correlation with the IL-33 level. A stepwise multivariable logistic regression analysis revealed that sST2 is an independent risk factor for subclinical atherosclerosis. Although the diagnostic predictive value of HT risk was determined as >51.8 pg l(-1) in the receiver operating characteristic curve analysis in respect of the sST2 level, the diagnostic predictive value for subclinical atherosclerosis risk was determined to be >107.2 pg l(-1). The sST2 level displays a positive correlation with atherosclerotic changes, and is an independent risk factor for subclinical atherosclerosis expressed as increased CIMT.

Reversible cerebral vasoconstriction syndromes (RCVS) and primary angiitis of the central nervous system (PACNS) are invariably considered in the differential diagnosis of new cerebral arteriopathies. However, prompt and accurate diagnosis remains challenging. Here we compared the features of 159 RCVS to 47 PACNS patients and developed criteria for prompt bedside diagnosis. Recurrent thunderclap headache (TCH), and single TCH combined with either normal neuroimaging, border zone infarcts, or vasogenic edema, have 100% positive predictive value for diagnosing RCVS or RCVS-spectrum disorders. In patients without TCH and positive angiography, neuroimaging can discriminate RCVS (no lesion) from PACNS (deep/brainstem infarcts). Ann Neurol 2016;79:882-894., (© 2016 American Neurological Association.)

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., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: Quantitative and qualitative evaluation of middle cerebral artery (MCA) density, together with extent of thrombi, was assessed on plain computerized tomography (CT) to delineate better the prognostic value of the hyperdense MCA sign (HMCAS) in a cohort of patients who underwent intravenous or intra-arterial thrombolysis., Methods: Density of MCA was quantified by maximum pixel-sized measurement of Hounsfield unit (HU) in 105 patients with acute MCA proximal segment occlusion, 15 patients with vertebrobasilar circulation stroke (VBS) and 44 nonstroke control subjects. Predictive value of HMCAS, absolute HU value of within MCA, side-to-side HU ratio, and difference along with a newly introduced hyperdense MCA burden score in early dramatic recovery (EDR) and third-month favorable prognosis were determined with multivariate adjustment for age, baseline stroke severity, and thrombus length as measured on CT angiography. Receiver operator characteristics (ROC) curves were used to determine the cutoffs of quantitative indices to determine HMCAS and their prognostic significance., Results: Higher HU was present in the ipsilateral MCA of the patients compared with their contralateral side and basilar tip and any MCA of VBS stroke and control subjects (area under the curve [AUC] of ROC curves was .753). Symptomatic-to-asymptomatic HU difference and ratio of MCA stroke were also significantly higher than side-to-side difference calculated in VBS stroke and control groups (AUC of ROC curves: .770 and .764, respectively). Optimal thresholds of absolute HU (44), side-to-side HU difference (2), and ratio (1.0588) showed borderline sensitivity and specificity. HMCAS and its quantitative indices were not significantly associated with EDR and favorable third-month outcome. Furthermore, there was no difference in terms of cardioembolic and atherothrombotic thrombi HU., Conclusions: Utility of the HMCAS as a prognostic marker in stroke thrombolysis is not high in the CT angiography era. Previous observation regarding its positive prognostic role can be attributed to its association with proximal location and extent of clot burden, which are detectable reliably with current CT angiography techniques. Neither quantification nor extent of increased density seems to have clinical utility for treatment decision making in MCA strokes and prediction of emboli composition and response to recanalization attempt., (Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.)

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.

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.

Importance: Reversible cerebral vasoconstriction syndrome (RCVS) is a clinical-angiographic syndrome characterized by recurrent thunderclap headaches and reversible segmental multifocal cerebral artery narrowing. More than 30% of patients with RCVS develop subarachnoid hemorrhage (SAH). Patients with RCVS with SAH (RCVS-SAH) are often misdiagnosed as having potentially ominous conditions such as aneurysmal SAH (aSAH) or cryptogenic "angiogram-negative" SAH (cSAH) owing to overlapping clinical and imaging features., Objective: To identify predictors that can distinguish RCVS-SAH from aSAH and cSAH at the time of clinical presentation., Design: Retrospective analysis of 3 patient cohorts: patients with RCVS (1998-2009), patients with aSAH (1995-2003), and patients with cSAH (1995-2003)., Setting: Academic hospital and tertiary referral center., Participants: Consecutive patients with RCVS-SAH (n = 38), aSAH (n = 515), or cSAH (n = 93) whose conditions were diagnosed using standard criteria., Main Outcomes and Measures: Multivariate logistic regression analysis was used to identify predictors that differentiate RCVS-SAH from aSAH and cSAH., Results: Predictors differentiating RCVS-SAH from aSAH were younger age, chronic headache disorder, prior depression, prior chronic obstructive pulmonary disease, lower Hunt-Hess grade, lower Fisher SAH group, higher number of affected arteries, and the presence of bilateral arterial narrowing. Predictors differentiating RCVS-SAH from cSAH were younger age, female sex, prior hypertension, chronic headache disorder, lower Hunt-Hess grade, lower Fisher SAH group, and the presence of bilateral arterial narrowing., Conclusions and Relevance: We identified important clinical and imaging differences between RCVS-SAH, aSAH, and cSAH that may be useful for improving diagnostic accuracy, clinical management, and resource utilization.

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.

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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2012 Elsevier B.V. All rights reserved.)

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/.

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.

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 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.

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.

Background: l-2-Hydroxyglutaric aciduria is a rare progressive neurometabolic disorder of childhood inherited as an autosomal recessive trait. Urine organic-acid screening is necessary for its diagnosis. Although it is a disorder of childhood, recently adult cases have been reported., Cases: Here we report 4 adult patients in whom diagnoses were established in adulthood. These patients had some interesting features. First, their diagnoses were delayed until adulthood because of mild clinical symptoms. In such cases, the typical MRI findings are the best diagnostic clue for l-2-Hydroxyglutaric aciduria. Second, there was a correlation between the severity of the clinical course and the extent of MRI findings. The cerebral white-matter lesions were diffuse and confluent on the MRI of 3 of the 4 patients, who also experienced a rapidly progressive clinical decline. Third, there were different clinical presentations even within the same family., Conclusions: For the evaluation of patients with symptoms referable to cerebellar, pyramidal, extrapyramidal, or cognitive impairment as well as seizures associated with subcortical white-matter and symmetrical dentate nuclei and basal ganglia involvement on MRI, urine organic acid analysis should be included in the evaluation, regardless of patient's age.

Creutzfeldt-Jakob disease is the most common disorder among the very rare human transmissible subacute spongiform encephalopathies. Sporadic, familial or iatrogenic forms of the disease can be seen. The common presentations of the disease include rapidly progressive cognitive decline, behavioral changes, visual disturbances and cerebellar dysfunction. Hyperparathyroidism and toxicity of lithium and bismuth have been reported to induce similar symptoms and EEG findings, leading to a Creutzfeldt-Jakob like syndrome. We report a very rare case of Creutzfeldt-Jakob disease presenting with hyperparathyroidism and generalized tonic status epilepticus.