Clustering Genes of Common Evolutionary History.

Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent-due to events such as incomplete lineage sorting or horizontal gene transfer-it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such "process-agnostic" approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward's method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl)

Aerodynamic characterization of hypersonic transportation systems and its impact on mission analysis

This paper aims to provide technical insights on the aerodynamic characterization activities performed in the field of the H2020 STRATOFLY project, for the Mach 8 waverider reference configuration. Considering the complexity of the configuration to be analyzed at conceptual/preliminary design stage, a build-up approach has been adopted. The complexity of the aerodynamic model increases incrementally, from the clean external configuration up to the complete configuration, including propulsion systems elements and flight control surfaces. At each step, the aerodynamic analysis is complemented with detailed mission analysis, in which the different versions of the aerodynamic databases are used as input for the trajectory simulation. eventually, once the contribution to the aerodynamic characterization of flight control surfaces is evaluated, stability and trim analysis is carried out. The comparison of the results obtained through the different mission analysis campaigns clearly shows that the accuracy of aerodynamic characterization may determine the feasibility or unfeasibility of a mission concept

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Signals of CP Violation Beyond the MSSM in Higgs and Flavor Physics

We study an extension of the Higgs sector of the Minimal Supersymmetric Standard Model (MSSM), considering the effects of new degrees of freedom at the TeV scale, and allowing for sources of CP violation beyond the MSSM (BMSSM). We analyze the impact of the BMSSM sources of CP violation on the Higgs collider phenomenology and on low energy flavor and CP violating observables. We identify distinct Higgs collider signatures that cannot be realized, either in the case without CP violating phases or in the CP violating MSSM, and investigate the prospects to probe them at the Tevatron and the LHC. The most striking benchmark scenario has three neutral Higgs bosons that all decay dominantly into W boson pairs and that are well within the reach of the 7 TeV LHC run. On the other hand, we also present scenarios with three Higgs bosons that have masses M_Hi > 150 GeV and decay dominantly into b bbar. Such scenarios are much more challenging to probe and can even lie completely outside the reach of the 7 TeV LHC run. We explore complementary scenarios with standard MSSM Higgs signals that allow to accommodate a sizable B_s mixing phase as indicated by D0, as well as the excess in B_s --> mu+ mu- candidates recently reported by CDF. We find that, in contrast to the MSSM, a minimal flavor violating soft sector is sufficient to generate significant corrections to CP violating observables in meson mixing, compatible with EDM constraints. In particular, a sizable B_s mixing phase, S_psiphi < 0.4, can be achieved for specific regions of parameter space. Such a large B_s mixing phase would unambiguously imply a sizable suppression of S_psiKs with respect to the SM prediction and a BR(B_s --> mu+ mu-) close to the 95% C.L. upper bound reported by CDF.Comment: 58 pages, 15 figures, 2 tables, v2 matches published versio

Pressure shock fronts formed by ultra-fast shear cracks in viscoelastic materials

Spontaneously propagating cracks in solids emit both pressure and shear waves. When a shear crack propagates faster than the shear wave speed of the material, the coalescence of the shear wavelets emitted by the near-crack-tip region forms a shock front that significantly concentrates particle motion. Such a shock front should not be possible for pressure waves, because cracks should not be able to exceed the pressure wave speed in isotropic linear-elastic solids. In this study, we present full-field experimental measurements of dynamic shear cracks in viscoelastic polymers that result in the formation of a pressure shock front, in addition to the shear one. The apparent violation of classic theories is explained by the strain-rate-dependent material behavior of polymers, where the crack speed remains below the highest pressure wave speed prevailing locally around the crack tip. These findings have important implications for the physics and dynamics of shear cracks such as earthquakes

A Computer-Aided Detection system for lung nodules in CT images

Lung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer-Aided Detection (CAD) system, composed by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nodules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection), an Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services

Informing the design of a multisensory learning environment for elementary mathematics learning

It is well known that primary school children may face difficulties in acquiring mathematical competence, possibly because teaching is generally based on formal lessons with little opportunity to exploit more multisensory-based activities within the classroom. To overcome such difficulties, we report here the exemplary design of a novel multisensory learning environment for teaching mathematical concepts based on meaningful inputs from elementary school teachers. First, we developed and administered a questionnaire to 101 teachers asking them to rate based on their experience the learning difficulty for specific arithmetical and geometrical concepts encountered by elementary school children. Additionally, the questionnaire investigated the feasibility to use multisensory information to teach mathematical concepts. Results show that challenging concepts differ depending on children school level, thus providing a guidance to improve teaching strategies and the design of new and emerging learning technologies accordingly. Second, we obtained specific and practical design inputs with workshops involving elementary school teachers and children. Altogether, these findings are used to inform the design of emerging multimodal technological applications, that take advantage not only of vision but also of other sensory modalities. In the present work, we describe in detail one exemplary multisensory environment design based on the questionnaire results and design ideas from the workshops: the Space Shapes game, which exploits visual and haptic/proprioceptive sensory information to support mental rotation, 2D–3D transformation and percentages. Corroborating research evidence in neuroscience and pedagogy, our work presents a functional approach to develop novel multimodal user interfaces to improve education in the classroom