Three-dimensional assessment of nose and lip morphology in North Sudanese subjects with Down syndrome

Objective: To detail the nasolabial morphologic characteristics of North Sudanese subjects with Down syndrome (DS). Materials and Methods: Nasolabial morphology was assessed three-dimensionally in 64 North Sudanese subjects with DS aged 4 to 34 years and in 682 sex- and age-matched controls. Three-dimensional facial coordinates were collected using a laser scan, and selected distances, angles, areas, and volumes were computed. Subject and reference data were compared by computing z-scores and Student's t-tests. Results: The nose was significantly smaller (area) in subjects with DS than in reference subjects, and it had a different shape (more flat angle of alar slope, more acute nasal tip angle). The vertical (nasal bridge length, nose height) and anteroposterior (nasal tip protrusion) dimensions were reduced, while the horizontal dimensions (alar base width, inferior widths of the nostrils) were increased. The nasolabial angle was increased. The cutaneous lip volume was significantly smaller, while the vermilion lip area was larger in the subjects with DS. The mouth and philtrum widths were significantly reduced, while the vermilion height was significantly increased. Conclusion: Analyzed subjects with DS had a hypoplastic nose and different upper and lower lips than did reference, normal subjects. (Angle Orthod. 2011;81:107-114.

ImageCLEF 2014: Overview and analysis of the results

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.This work has been partially supported by the tranScriptorium FP7 project under grant #600707 (M. V., R. P.).Caputo, B.; Müller, H.; Martinez-Gomez, J.; Villegas Santamaría, M.; Acar, B.; Patricia, N.; Marvasti, N.... (2014). ImageCLEF 2014: Overview and analysis of the results. En Information Access Evaluation. Multilinguality, Multimodality, and Interaction: 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings. Springer Verlag (Germany). 192-211. https://doi.org/10.1007/978-3-319-11382-1_18S192211Bosch, A., Zisserman, A.: Image classification using random forests and ferns. In: Proc. CVPR (2007)Caputo, B., Müller, H., Martinez-Gomez, J., Villegas, M., Acar, B., Patricia, N., Marvasti, N., Üsküdarlı, S., Paredes, R., Cazorla, M., Garcia-Varea, I., Morell, V.: ImageCLEF 2014: Overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, Springer, Heidelberg (2014)Caputo, B., Patricia, N.: Overview of the ImageCLEF 2014 Domain Adaptation Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)de Carvalho Gomes, R., Correia Ribas, L., Antnio de Castro Jr., A., Nunes Gonalves, W.: CPPP/UFMS at ImageCLEF 2014: Robot Vision Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)Del Frate, F., Pacifici, F., Schiavon, G., Solimini, C.: Use of neural networks for automatic classification from high-resolution images. IEEE Transactions on Geoscience and Remote Sensing 45(4), 800–809 (2007)Feng, S.L., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II–1002. IEEE (2004)Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61(3), 399–409 (1997)Goh, K.-S., Chang, E.Y., Li, B.: Using one-class and two-class svms for multiclass image annotation. IEEE Transactions on Knowledge and Data Engineering 17(10), 1333–1346 (2005)Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: Proc. CVPR. Extended Version Considering its Additional MaterialJie, L., Tommasi, T., Caputo, B.: Multiclass transfer learning from unconstrained priors. In: Proc. ICCV (2011)Kim, S., Park, S., Kim, M.: Image classification into object / non-object classes. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 393–400. Springer, Heidelberg (2004)Ko, B.C., Lee, J., Nam, J.Y.: Automatic medical image annotation and keyword-based image retrieval using relevance feedback. Journal of Digital Imaging 25(4), 454–465 (2012)Kökciyan, N., Türkay, R., Üsküdarlı, S., Yolum, P., Bakır, B., Acar, B.: Semantic Description of Liver CT Images: An Ontological Approach. IEEE Journal of Biomedical and Health Informatics (2014)Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.  2, pp. 2169–2178. IEEE (2006)Martinez-Gomez, J., Garcia-Varea, I., Caputo, B.: Overview of the imageclef 2012 robot vision task. In: CLEF (Online Working Notes/Labs/Workshop) (2012)Martinez-Gomez, J., Garcia-Varea, I., Cazorla, M., Caputo, B.: Overview of the imageclef 2013 robot vision task. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes (2013)Martinez-Gomez, J., Cazorla, M., Garcia-Varea, I., Morell, V.: Overview of the ImageCLEF 2014 Robot Vision Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014)Mueen, A., Zainuddin, R., Baba, M.S.: Automatic multilevel medical image annotation and retrieval. Journal of Digital Imaging 21(3), 290–295 (2008)Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. Springer (2010)Park, S.B., Lee, J.W., Kim, S.K.: Content-based image classification using a neural network. Pattern Recognition Letters 25(3), 287–300 (2004)Patricia, N., Caputo, B.: Learning to learn, from transfer learning to domain adaptation: a unifying perspective. In: Proc. CVPR (2014)Pronobis, A., Caputo, B.: The robot vision task. In: Muller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF. The Information Retrieval Series, vol. 32, pp. 185–198. Springer, Heidelberg (2010)Pronobis, A., Christensen, H., Caputo, B.: Overview of the imageclef@ icpr 2010 robot vision track. In: Recognizing Patterns in Signals, Speech, Images and Videos, pp. 171–179 (2010)Qi, X., Han, Y.: Incorporating multiple svms for automatic image annotation. Pattern Recognition 40(2), 728–741 (2007)Reshma, I.A., Ullah, M.Z., Aono, M.: KDEVIR at ImageCLEF 2014 Scalable Concept Image Annotation Task: Ontology based Automatic Image Annotation. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes. Sheffield, UK, September 15-18 (2014)Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)Sahbi, H.: CNRS - TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain, September 23-26 (2013)Sethi, I.K., Coman, I.L., Stan, D.: Mining association rules between low-level image features and high-level concepts. In: Aerospace/Defense Sensing, Simulation, and Controls, pp. 279–290. International Society for Optics and Photonics (2001)Shi, R., Feng, H., Chua, T.-S., Lee, C.-H.: An adaptive image content representation and segmentation approach to automatic image annotation. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 545–554. Springer, Heidelberg (2004)Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: Proc. ICCV (2013)Tommasi, T., Quadrianto, N., Caputo, B., Lampert, C.H.: Beyond dataset bias: Multi-task unaligned shared knowledge transfer. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 1–15. Springer, Heidelberg (2013)Tsikrika, T., de Herrera, A.G.S., Müller, H.: Assessing the scholarly impact of imageCLEF. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds.) CLEF 2011. LNCS, vol. 6941, pp. 95–106. Springer, Heidelberg (2011)Ünay, D., Soldea, O., Akyüz, S., Çetin, M., Erçil, A.: Medical image retrieval and automatic annotation: Vpa-sabanci at imageclef 2009. In: The Cross-Language Evaluation Forum (CLEF) (2009)Vailaya, A., Figueiredo, M.A., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10(1), 117–130 (2001)Villegas, M., Paredes, R.: Overview of the ImageCLEF 2012 Scalable Web Image Annotation Task. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy, September 17-20 (2012), http://mvillegas.info/pub/Villegas12_CLEF_Annotation-Overview.pdfVillegas, M., Paredes, R.: Overview of the ImageCLEF 2014 Scalable Concept Image Annotation Task. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes, Sheffield, UK, September 15-18 (2014), http://mvillegas.info/pub/Villegas14_CLEF_Annotation-Overview.pdfVillegas, M., Paredes, R., Thomee, B.: Overview of the ImageCLEF 2013 Scalable Concept Image Annotation Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain, September 23-26 (2013), http://mvillegas.info/pub/Villegas13_CLEF_Annotation-Overview.pdfVillena Román, J., González Cristóbal, J.C., Goñi Menoyo, J.M., Martínez Fernández, J.L.: MIRACLE’s naive approach to medical images annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2005)Wong, R.C., Leung, C.H.: Automatic semantic annotation of real-world web images. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1933–1944 (2008)Yang, C., Dong, M., Fotouhi, F.: Image content annotation using bayesian framework and complement components analysis. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 1, pp. I–1193. IEEE (2005)Yılmaz, K.Y., Cemgil, A.T., Simsekli, U.: Generalised coupled tensor factorisation. In: Advances in Neural Information Processing Systems, pp. 2151–2159 (2011)Zhang, Y., Qin, J., Chen, F., Hu, D.: NUDTs Participation in ImageCLEF Robot Vision Challenge 2014. In: CLEF 2014 Evaluation Labs and Workshop, Online Working Notes (2014

Performance of BOADICEA and BRCAPRO genetic models and of empirical criteria based on cancer family history for predicting BRCA mutation carrier probabilities: A retrospective study in a sample of Italian cancer genetics clinics

Abstract Purpose To evaluate in current practice the performance of BOADICEA and BRCAPRO risk models and empirical criteria based on cancer family history for the selection of individuals for BRCA genetic testing. Patients and methods The probability of BRCA mutation according to the three tools was retrospectively estimated in 918 index cases consecutively undergone BRCA testing at 15 Italian cancer genetics clinics between 2006 and 2008. Results 179 of 918 cases (19.5%) carried BRCA mutations. With the strict use of the criteria based on cancer family history 173 BRCA (21.9%) mutations would have been detected in 789 individuals. At the commonly used 10% threshold of BRCA mutation carrier probability, the genetic models showed a similar performance [PPV (38% and 37%), sensitivity (76% and 77%) and specificity (70% and 69%)]. Their strict use would have avoided around 60% of the tests but would have missed approximately 1 every 4 carriers. Conclusion Our data highlight the complexity of BRCA testing referral in routine practice and question the strict use of genetic models for BRCA risk assessment

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease