A founder CEP120 mutation in Jeune asphyxiating thoracic dystrophy expands the role of centriolar proteins in skeletal ciliopathies.

Jeune asphyxiating thoracic dystrophy (JATD) is a skeletal dysplasia characterized by a small thoracic cage and a range of skeletal and extra-skeletal anomalies. JATD is genetically heterogeneous with at least nine genes identified, all encoding ciliary proteins, hence the classification of JATD as a skeletal ciliopathy. Consistent with the observation that the heterogeneous molecular basis of JATD has not been fully determined yet, we have identified two consanguineous Saudi families segregating JATD who share a single identical ancestral homozygous haplotype among the affected members. Whole-exome sequencing revealed a single novel variant within the disease haplotype in CEP120, which encodes a core centriolar protein. Subsequent targeted sequencing of CEP120 in Saudi and European JATD cohorts identified two additional families with the same missense mutation. Combining the four families in linkage analysis confirmed a significant genome-wide linkage signal at the CEP120 locus. This missense change alters a highly conserved amino acid within CEP120 (p.Ala199Pro). In addition, we show marked reduction of cilia and abnormal number of centrioles in fibroblasts from one affected individual. Inhibition of the CEP120 ortholog in zebrafish produced pleiotropic phenotypes characteristic of cilia defects including abnormal body curvature, hydrocephalus, otolith defects and abnormal renal, head and craniofacial development. We also demonstrate that in CEP120 morphants, cilia are shortened in the neural tube and disorganized in the pronephros. These results are consistent with aberrant CEP120 being implicated in the pathogenesis of JATD and expand the role of centriolar proteins in skeletal ciliopathies

Efficient Organic Excitonic Solar Cells with Carbon Nanotubes Replacing In2O3:Sn as the Transparent Electrode

The conclusions of this report are that: (1) organic solar cells with efficiencies of up to 1.43% conversion efficiency that use no ITO and no PEDOT:PSS, are demonstrated; (2) a cell without ITO, but with PEDOT:PSS gave 2.6% conversion efficiency; (3) due to porous nature of SWCNT substrates, optimization of the active layer is essential; and (4) SWCNT electrodes bring one step closer the goal of a fully printable, organic solar cell

Computing optimal coalition structures in polynomial time

The optimal coalition structure determination problem is in general computationally hard. In this article, we identify some problem instances for which the space of possible coalition structures has a certain form and constructively prove that the problem is polynomial time solvable. Specifically, we consider games with an ordering over the players and introduce a distance metric for measuring the distance between any two structures. In terms of this metric, we define the property of monotonicity, meaning that coalition structures closer to the optimal, as measured by the metric, have higher value than those further away. Similarly, quasi-monotonicity means that part of the space of coalition structures is monotonic, while part of it is non-monotonic. (Quasi)-monotonicity is a property that can be satisfied by coalition games in characteristic function form and also those in partition function form. For a setting with a monotonic value function and a known player ordering, we prove that the optimal coalition structure determination problem is polynomial time solvable and devise such an algorithm using a greedy approach. We extend this algorithm to quasi-monotonic value functions and demonstrate how its time complexity improves from exponential to polynomial as the degree of monotonicity of the value function increases. We go further and consider a setting in which the value function is monotonic and an ordering over the players is known to exist but ordering itself is unknown. For this setting too, we prove that the coalition structure determination problem is polynomial time solvable and devise such an algorithm

Identification of determinants for rescheduling travel mode choice and transportation policies to reduce car use in urban areas

[EN] This paper presents a qualitative analysis about the determinants related to rescheduling travel mode decisions during the activity scheduling process. Notably, we were interested to study changes between intention and behavior. Data used came from an in-depth Computer Assisted Telephone Interview (CATI) follow up survey to habitual drivers carried out during the implementation of a panel survey. An interpretative qualitative method based on Analytic Induction was used to cope with the complex nature of rescheduling decisions and the characteristics of the data. The Theory of Planned Behavior has been used to gain a better understanding of the reasons associated with rescheduling travel mode decisions and to obtain a possible explanation of the phenomena studied. In our sample, 12 codes were identified as the main determinants of travel mode changing. Main reasons for rescheduling a travel mode are different considering gender, age, and the type of travel mode change. Main reasons for changing a nonprivate preplanned travel mode to a private travel mode are different considering the type of travel mode preplanned. New determinants of rescheduling decisions different from those associated with other activity scheduling decisions previously identified emerge when analyzing travel mode changes. A number of important sustainable transportation policies to reduce car use in urban areas are derived from the results of this study.This research is partially funded by MINERVA project founded by the ICDCi National Program of Society Challenges of the Spanish Ministerio de Econom ıa, Industria y Competitividad (TRA2015-71184-C2-1-R).Mars, L.; Ruiz Sánchez, T.; Arroyo-López, MR. (2018). Identification of determinants for rescheduling travel mode choice and transportation policies to reduce car use in urban areas. International Journal of Sustainable Transportation. 1-11. https://doi.org/10.1080/15568318.2017.1416432S11

Plasma Metabolomics in Human Pulmonary Tuberculosis Disease: A Pilot Study

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q<0.05. Two-way hierarchical cluster analysis (HCA) was used to identify clusters of ions contributing to separation of cases and controls, and metabolomics databases were used to match these ions to known metabolites. Identity of specific D-series resolvins, glutamate and Mycobacterium tuberculosis (Mtb)-derived trehalose-6-mycolate was confirmed using LC-MS/MS analysis. Over 23,000 metabolites were detected in untargeted metabolomic analysis and 61 metabolites were significantly different between the two groups. HCA revealed 8 metabolite clusters containing metabolites largely upregulated in patients with TB disease, including anti-TB drugs, glutamate, choline derivatives, Mycobacterium tuberculosis-derived cell wall glycolipids (trehalose-6-mycolate and phosphatidylinositol) and pro-resolving lipid mediators of inflammation, known to stimulate resolution, efferocytosis and microbial killing. The resolvins were confirmed to be RvD1, aspirin-triggered RvD1, and RvD2. This study shows that high-resolution metabolomic analysis can differentiate patients with active TB disease from their asymptomatic household contacts. Specific metabolites upregulated in the plasma of patients with active TB disease, including Mtb-derived glycolipids and resolvins, have potential as biomarkers and may reveal pathways involved in TB disease pathogenesis and resolution

Noninvasive Markers of Hepatic Fibrosis in Chronic Hepatitis B

A serum biomarker (FibroTest; Biopredictive, Paris, France; FibroSure; LabCorp, Burlington, USA) and liver stiffness measurement (LSM) by Fibroscan (Echosens, Paris, France) have been extensively validated in chronic hepatitis C. This review updates the clinical validation of serum biomarkers and LSM in patients with chronic hepatitis B (CHB). One meta-analysis combined all published studies and another used a database combining FibroTest individual data. Sensitivity analysis assessed the impact of several factors, including authors’ independence, length of biopsy, ethnicity, hepatitis B early antigen status, viral load, and alanine aminotransferase value. Only two biomarkers had several validations: FibroTest (8 studies, 1,842 patients), and Fibroscan (5 studies, 618 patients). For the diagnosis of advanced fibrosis, the standardized area under the receiver operating curve was 0.84 (0.79–0.86) for FibroTest and 0.89 (0.83–0.96) for LSM, without significant difference. No significant factors of variability were identified for FibroTest’s performance. In conclusion, FibroTest and LSM were the most validated biomarkers of fibrosis in CHB. However, the reliability of Fibroscan must be better assessed

A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features

The need to leverage knowledge through data mining has driven enterprises in a demand for more data. However, there is a gap between the availability of data and the application of extracted knowledge for improving decision support. In fact, more data do not necessarily imply better predictive data-driven marketing models, since it is often the case that the problem domain requires a deeper characterization. Aiming at such characterization, we propose a framework drawn on three feature selection strategies, where the goal is to unveil novel features that can effectively increase the value of data by providing a richer characterization of the problem domain. Such strategies involve encompassing context (e.g., social and economic variables), evaluating past history, and disaggregate the main problem into smaller but interesting subproblems. The framework is evaluated through an empirical analysis for a real bank telemarketing application, with the results proving the benefits of such approach, as the area under the receiver operating characteristic curve increased with each stage, improving previous model in terms of predictive performance.The work of P. Cortez was supported by FCT within the Project Scope UID/CEC/00319/2013. The authors would like to thank the anonymous reviewers for their helpful comments.info:eu-repo/semantics/publishedVersio