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Tristetraprolin (TTP) is an RNA-binding protein that targets numerous immunomodulatory mRNA transcripts for degradation. Many TTP targets are key players in the pathogenesis of periodontal bone loss, including tumor necrosis factor–α. To better understand the extent that host immune factors play during periodontal bone loss, we assessed alveolar bone levels, inflammation and osteoclast activity in periodontal tissues, and immune response in draining cervical lymph nodes in TTP-deficient and wild-type (WT) mice in an aging study. WT and TTP-deficient (knockout [KO]) mice were used for all studies under specific pathogen-free conditions. Data were collected on mice aged 3, 6, and 9 mo. Microcomputed tomography (µCT) was performed on maxillae where 3-dimensional images were generated and bone loss was assessed. Decalcified sections of specimens were scored for inflammation and stained with tartrate-resistant acid phosphate (TRAP) to visualize osteoclasts. Immunophenotyping was performed on single-cell suspensions isolated from primary and peripheral lymphoid tissues using flow cytometry. Results presented indicate that TTP KO mice had significantly more alveolar bone loss over time compared with WT controls. Bone loss was associated with significant increases in inflammatory cell infiltration and an increased percentage of alveolar bone surfaces apposed with TRAP+ cells. Furthermore, it was found that the draining cervical lymph nodes were significantly enlarged in TTP-deficient animals and contained a distinct pathological immune profile compared with WT controls. Finally, the oral microbiome in the TTP KO mice was significantly different with age from WT cohoused mice. The severe bone loss, inflammation, and increased osteoclast activity observed in these mice support the concept that TTP plays a critical role in the maintenance of alveolar bone homeostasis in the presence of oral commensal flora. This study suggests that TTP is required to inhibit excessive inflammatory host responses that contribute to periodontal bone loss, even in the absence of specific periodontal pathogens.

Supplemental material, DS_10.1177_0022034518756889 for Tristetraprolin Is Required for Alveolar Bone Homeostasis by H.M. Steinkamp, J.D. Hathaway-Schrader, M.B. Chavez, J.D. Aartun, L. Zhang, T. Jensen, A. Shojaee Bakhtiari, K.L. Helke, D.J. Stumpo, A.V. Alekseyenko, C.M. Novince, P.J. Blackshear and K.L. Kirkwood in Journal of Dental Research

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

BACKGROUND: To identify clinically relevant glioblastoma (GBM) sub-classification/subtypes based on radiomic analysis. Integrated genomic analysis has already identified genomic subtypes of GBM as characterized by specific genomic aberrations and mutations. In our study, we seek to demonstrate a GBM sub-classification based on radiomic texture analysis that is both predictive and prognostic. METHODS: We retrospectively identified 80 GBM treatment naive patients from the Cancer Genome Atlas with corresponding MRI data in the Cancer Imaging Archive. We applied texture analysis (528 features) to each tumor. We extracted texture features and performed feature selection. Univariate and multivariate analysis were also performed. Clustering identified radiomic subclasses. RESULTS: Multiple texture features were associated with GBM and we were able to classify GBM into 4 subtypes. However, subclassification into 3 subclasses were associated with differences in patient progression free and overall survival. CONCLUSIONS: GBM subtypes based on radiomic texture signatures were identified that were both predictive and prognostic. These findings are important as this can help determine patient outcome based on imaging (obtained at the time of diagnosis before treatment); this can also help determine response to therapy at the pre-treatment stage to stratify patients and serve as imaging endpoints for clinical trials.

BACKGROUND Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. METHODS We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. RESULTS Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively). CONCLUSIONS Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

In many applications it is necessary to be able to classify images in a database accurately and with acceptable speed. The main problem is to assign different images to right categories. The later problem becomes more challenging while dealing with large databases with many categories and subcategories. In this paper we propose a novel classification method based on an adopted hierarchical Dirichlet generative model, previously proposed for corpora document classification. In order to adopt the model to work with image data we use the bag of visual words model. We show that if properly applied the model can achieve adequate results for hierarchical image classification. Experimental results are presented and discussed to show the merits of the proposed approach.

A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.

An efficient iris segmentation method based on analyzing the local entropy characteristic of the iris image, is proposed in this paper and the strength and weaknesses of the method are analyzed for practical purposes. The method shows special strength in providing designers with an adequate degree of freedom in choosing the proper sections of the iris for their application purposes., {"references":["C. E. Shannon, \"A Mathematical Theory of Communication\", Bell System Technical Journal, vol. 27, pp. 379-423, 623-656, July, October,1948.","J. Daugman, \"How Iris Recognition Works\", IEEE Transaction on\ncircuits and systems for video technology, vol. 14, No.1, January 2004.","F. Hao, R. Anderson and J. Daugman, \"Combining Crypto with\nbiometrics effectively\", IEEE transactions on computers, Vol.55, No.9,\nSeptember 2006.","Li.Ma, T. Wang and D.Zhang, \"Efficient Iris Recognition by\nCharacterizing Key Local Variations\" IEEE Trans. On image\nprocessing, vol.13, no. 6, pp. 739-750, 2004.","A. Shojaee Bakhtiari, A. A. Beheshti, B. Zamanlooe, \"Phase\nCongruency Based Image Enhancement Method and Its Application in\nEnhancing Iris Feature Extraction\", Proceedings of Iranian conference\non electrical engineering, 2007.","P. Kovesi, \"Phase Congruency Detects Corners and Edges\", School of\nComputer Science & Software Engineering, The University of Western\nAustralia, November 2003.","W. K. Kong and D.Zhang, \"Accurate Iris Segmentation Based on Novel\nReflection and Eyelash Detection Model\", Proceedings of 2001\nInternational Symposium on Intelligent Multimedia, Video and Speech\nprocessing, May, 2001.","Libor Masek, \"Recognition of Human Iris Patters for Biometric\nIdentification\" University of Western Australia 2003.","P. Kovesi. MATLAB code for calculating phase congruency and phase\nsymmetry asymmetry, April 1996,\nhttp://www.cs.uwa.edu.au/_pk/research/matlabfans/\n[10] Iris Recognition Research Group Center for Biometrics and Security\nResearch National Laboratory of Pattern Recognition, Institute of\nAutomation, Chinese Academy of Sciences. http://www.cbsr.ia.ac.cn"]}