Yukawa Textures, Neutrino Masses and Horava-Witten M-Theory

We consider the Horava-Witten based model with 5-branes situated near the distant orbifold plane and with vanishing instanton numbers on the physical plane. This model has a toric fibered Calabi-Yau with del Pezzo base dP_7 which allows three generations with Standard Model gauge group at the GUT scale. Previous analysis showed that the quark hierarchy at the electroweak scale could be achieved qualitatively without undue fine tuning due to the effects of the 5-branes on the Kahler potential. We extend here this analysis to include the leptons. A new mechanism is introduced to obtain neutrino masses by assuming massless right handed neutrinos exist in the particle spectrum, which allows a cubic holomorphic term to exist in the Kahler metric, l_L*H_2*nu_R, scaled by the 11D Planck mass. After transferring this term to the superpotential, this term gives rise to neutrino masses of the correct size at the electroweak scale. With natural choices of the Yukawa and Kahler matrix entries, it is possible to fit all mass, CKM and MNS experimental data. The model predicts mu -> e + gamma decay at a rate that should be detectable for much of the SUSY parameter space in the next round of experiments.Comment: 24 pages, 4 figures. Minor changes, references added. Some discussion on neutrino mass generating mechanism added; no other change. Accepted for publication in Nucl. Phys.

Giving voters what they want? Party orientation perceptions and preferences in the British electorate

Some of the most important propositions in the political marketing literature hinge on assumptions about the electorate. In particular, voters are presumed to react in different ways to different orientations or postures. Yet there are theoretical reasons for questioning some of these assumptions, and certainly they have seldom been empirically tested. Here, we focus on one prominent example of political marketing research: Lees-Marshment’s orientations’ model. We investigate how the public reacts to product and market orientation, whether they see a trade-off between the two (a point in dispute among political marketing scholars), and whether partisans differ from non-partisan voters by being more inclined to value product over market orientation. Evidence from two mass sample surveys of the British public (both conducted online by YouGov) demonstrates important heterogeneity within the electorate, casts doubt on the core assumptions underlying some political marketing arguments and raises broader questions about what voters are looking for in a party

Pharmacological basis and clinical evidence of dabigatran therapy

Dabigatran is an emerging oral anticoagulant which is a direct inhibitor of thrombin activity. It has been approved in the European Union and the United States of America for the prevention of thrombosis after major orthopedic surgery. It has also been approved by the American Food and Drug Administration and the European Medicines Agency for the prevention of stroke in chronic atrial fibrillation. Dabigatran provides a stable anticoagulation effect without any need to perform periodical laboratory controls. Of note, there is a growing amount of clinical evidence which shows its safety and efficacy. For these reasons, dabigatran may suppose a revolution in oral anticoagulation. However, two important limitations remain. First, it is contraindicated in patients with end-stage renal disease. Second, there is no evidence of the prevention of thrombosis in mechanical heart valves

Genome-wide microRNA and gene analysis of mesenchymal stem cell chondrogenesis identifies an essential role and multiple targets for miR-140-5p

microRNAs (miRNAs) are abundantly expressed in development where they are critical determinants of cell differentiation and phenotype. Accordingly miRNAs are essential for normal skeletal development and chondrogenesis in particular. However, the question of which miRNAs are specific to the chondrocyte phenotype has not been fully addressed. Using microarray analysis of miRNA expression during mesenchymal stem cell chondrogenic differentiation and detailed examination of the role of essential differentiation factors, such as SOX9, TGF-b, and the cell condensation phase, we characterize the repertoire of specific miRNAs involved in chondrocyte development, highlighting in particular miR-140 and miR-455. Further with the use of mRNA microarray data we integrate miRNA expression and mRNA expression during chondrogenesis to underline the particular importance of miR-140, especially the -5p strand. We provide a detailed identification and validation of direct targets of miR-140-5p in both chondrogenesis and adult chondrocytes with the use of microarray and 30 UTR analysis. This emphasizes the diverse array of targets and pathways regulated by miR-140-5p. We are also able to confirm previous experimentally identified targets but, additionally, identify a novel positive regulation of the Wnt signaling pathway by miR-140-5p. Wnt signaling has a complex role in chondrogenesis and skeletal development and these findings illustrate a previously unidentified role for miR-140-5p in regulation of Wnt signaling in these processes. Together these developments further highlight the role of miRNAs during chondrogenesis to improve our understanding of chondrocyte development and guide cartilage tissue engineering

Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20 % better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a give

Salivary microRNAs: Diagnostic markers of mild traumatic brain injury in contact-sport

Concussion is difficult to diagnose, particularly when symptoms are atypical or late in presenting. An accurate and timely initial assessment is crucial for clinical management. Cerebral spinal fluid (CSF) and blood markers of traumatic brain injury show promising results but their clinical applicability in concussion has significant limitations. In the study, we explored saliva as a new source of biomarkers of concussion. Saliva samples of concussed players were collected after 48–72 h from concussion and analyzed by high-throughput technologies. A discovery group of 10 concussed rugby professional and semiprofessional athletes and 10 non-concussed matched controls was used for the analysis of 92 inflammatory proteins by the Proseek-Multiplex-Inflammation technology. In addition, saliva samples of 6 concussed and 6 non-concussed athletes were used to screen 800 human microRNAs (miRNAs) by the Nanostring Technology. The results were then validated by RT-qPCR in an enlarged cohort (validation group) comprising 22 concussed athletes. Results showed, no significant variations of the 65 inflammatory proteins detected in saliva between groups but 5 microRNAs, miR-27b-3p (p = 0.016), let-7i-5p (p = 0.001), miR-142-3p (p = 0.008), miR-107 (p = 0.028), miR-135b-5p (p = 0.017) significantly upregulated in concussed athletes. Univariate ROC curve analysis showed that the differentially expressed miRNAs could be considered good classifiers of concussion. Further analyses showed significant correlation between these microRNAs and Reaction Time component of the ImPACT concussion assessment tool. In addition, biocomputation analysis predicted the involvement of these microRNAs in important biological processes that might be related to trauma, such as response to hypoxia, cell death, neurogenesis, axon repair and myelination. Ease of access and non-invasiveness of saliva samples make these biomarkers particularly suitable for concussion assessment

Fully corrective boosting with arbitrary loss and regularization

We propose a general framework for analyzing and developing fully corrective boosting-based classifiers. The framework accepts any convex objective function, and allows any convex (for example, lp-norm, p ≥ 1) regularization term. By placing the wide variety of existing fully corrective boosting-based classifiers on a common footing, and considering the primal and dual problems together, the framework allows direct com- parison between apparently disparate methods. By solving the primal rather than the dual the framework is capable of generating efficient fully-corrective boosting algorithms without recourse to sophisticated convex optimization processes. We show that a range of additional boosting-based algorithms can be incorporated into the framework despite not being fully corrective. Finally, we provide an empirical analysis of the per- formance of a variety of the most significant boosting-based classifiers on a few machine learning benchmark datasets.Chunhua Shen, Hanxi Li, Anton van den Henge