An integrated native mass spectrometry and top-down proteomics method that connects sequence to structure and function of macromolecular complexes.

Mass spectrometry (MS) has become a crucial technique for the analysis of protein complexes. Native MS has traditionally examined protein subunit arrangements, while proteomics MS has focused on sequence identification. These two techniques are usually performed separately without taking advantage of the synergies between them. Here we describe the development of an integrated native MS and top-down proteomics method using Fourier-transform ion cyclotron resonance (FTICR) to analyse macromolecular protein complexes in a single experiment. We address previous concerns of employing FTICR MS to measure large macromolecular complexes by demonstrating the detection of complexes up to 1.8 MDa, and we demonstrate the efficacy of this technique for direct acquirement of sequence to higher-order structural information with several large complexes. We then summarize the unique functionalities of different activation/dissociation techniques. The platform expands the ability of MS to integrate proteomics and structural biology to provide insights into protein structure, function and regulation

Maternal E-cigarette exposure in mice alters DNA methylation and lung cytokine expression in offspring

Copyright © 2018 by the American Thoracic Society E-cigarette usage is increasing, especially among the young, with both the general population and physicians perceiving them as a safe alternative to tobacco smoking. Worryingly, e-cigarettes are commonly used by pregnant women. As nicotine is known to adversely affect children in utero, we hypothesized that nicotine delivered via e-cigarettes would negatively affect lung development. To test this, we developed a mouse model of maternal e-vapor (nicotine and nicotine-free) exposure and investigated the impact on the growth and lung inflammation in both offspring and mothers. Female Balb/c mice were exposed to e-fluid vapor containing nicotine (18 mg/ml nicotine E-cigarette [E-cig18], equivalent to two cigarettes per treatment, twice daily,) or nicotine free (E-cig0 mg/ml) from 6 weeks before mating until pups weaned. Male offspring were studied at Postnatal Day (P) 1, P20, and at 13 weeks. The mothers were studied when the pups weaned. In the mothers' lungs, e-cigarette exposure with and without nicotine increased the proinflammatory cytokines IL-1b, IL-6, and TNF-a. In adult offspring, TNF-a protein levels were increased in both E-cig18 and E-cig0 groups, whereas IL-1b was suppressed. This was accompanied by global changes in DNA methylation. In this study, we found that e-cigarette exposure during pregnancy adversely affected maternal and offspring lung health. As this occurred with both nicotine-free and nicotine-containing e-vapor, the effects are likely due to by-products of vaporization rather than nicotine

Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings

To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expense is further multiplied when a query image is evaluated against a gallery, e.g. in visual relocalization. While we don't obviate the need for geometric verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup. Our approach measures the asymmetric relation between two images. The model then learns a scene-specific measure of similarity, from training examples with known 3D visible-surface overlaps. The result is that we can quickly identify, for example, which test image is a close-up version of another, and by what scale factor. Subsequently, local features need only be detected at that scale. We validate our scene-specific model by showing how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans.Comment: ECCV 202

Health-state utilities in a prisoner population : a cross-sectional survey

Background: Health-state utilities for prisoners have not been described. Methods: We used data from a 1996 cross-sectional survey of Australian prisoners (n = 734). Respondent-level SF-36 data was transformed into utility scores by both the SF-6D and Nichol's method. Socio-demographic and clinical predictors of SF-6D utility were assessed in univariate analyses and a multivariate general linear model. Results: The overall mean SF-6D utility was 0.725 (SD 0.119). When subdivided by various medical conditions, prisoner SF-6D utilities ranged from 0.620 for angina to 0.764 for those with none/mild depressive symptoms. Utilities derived by the Nichol's method were higher than SF-6D scores, often by more than 0.1. In multivariate analysis, significant independent predictors of worse utility included female gender, increasing age, increasing number of comorbidities and more severe depressive symptoms. Conclusion: The utilities presented may prove useful for future economic and decision models evaluating prison-based health programs

In Vitro Assembly of Multiple DNA Fragments Using Successive Hybridization

Construction of recombinant DNA from multiple fragments is widely required in molecular biology, especially for synthetic biology purposes. Here we describe a new method, successive hybridization assembling (SHA) which can rapidly do this in a single reaction in vitro. In SHA, DNA fragments are prepared to overlap one after another, so after simple denaturation-renaturation treatment they hybridize in a successive manner and thereby assemble into a recombinant molecule. In contrast to traditional methods, SHA eliminates the need for restriction enzymes, DNA ligases and recombinases, and is sequence-independent. We first demonstrated its feasibility by constructing plasmids from 4, 6 and 8 fragments with high efficiencies, and then applied it to constructing a customized vector and two artificial pathways. As SHA is robust, easy to use and can tolerate repeat sequences, we expect it to be a powerful tool in synthetic biology

Optimal neighborhood indexing for protein similarity search

Background: Similarity inference, one of the main bioinformatics tasks, has to face an exponential growth of the biological data. A classical approach used to cope with this data flow involves heuristics with large seed indexes. In order to speed up this technique, the index can be enhanced by storing additional information to limit the number of random memory accesses. However, this improvement leads to a larger index that may become a bottleneck. In the case of protein similarity search, we propose to decrease the index size by reducing the amino acid alphabet.\ud \ud Results: The paper presents two main contributions. First, we show that an optimal neighborhood indexing combining an alphabet reduction and a longer neighborhood leads to a reduction of 35% of memory involved into the process, without sacrificing the quality of results nor the computational time. Second, our approach led us to develop a new kind of substitution score matrices and their associated e-value parameters. In contrast to usual matrices, these matrices are rectangular since they compare amino acid groups from different alphabets. We describe the method used for computing those matrices and we provide some typical examples that can be used in such comparisons. Supplementary data can be found on the website http://bioinfo.lifl.fr/reblosum.\ud \ud Conclusions: We propose a practical index size reduction of the neighborhood data, that does not negatively affect the performance of large-scale search in protein sequences. Such an index can be used in any study involving large protein data. Moreover, rectangular substitution score matrices and their associated statistical parameters can have applications in any study involving an alphabet reduction

Content-Aware Unsupervised Deep Homography Estimation

Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real world applications. To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art including deep solutions and feature-based solutions.Comment: Accepted by ECCV 2020 (Oral, Top 2%, 3 over 3 Strong Accepts). Jirong Zhang and Chuan Wang are joint first authors, and Shuaicheng Liu is the corresponding autho

Cardiovascular risk factors among Chamorros

BACKGROUND: Little is known regarding the cardiovascular disease risk factors among Chamorros residing in the United States. METHODS: The Chamorro Directory International and the CDC's Behavioral Risk Factor Surveillance System Questionnaire (BRFSS) were used to assess the health related practices and needs of a random sample of 228 Chamorros. RESULTS: Inactivity, hypertension, elevated cholesterol and diabetes mellitus were more prevalent in this Chamorro sample compared to the US average. Participants who were 50-and-older or unemployed were more likely to report hypertension, diabetes and inactivity, but they were also more likely to consume more fruits and vegetables than their younger and employed counterparts. Women were more likely to report hypertension and diabetes, whereas men were more likely to have elevated BMI and to have never had their blood cholesterol checked. CONCLUSION: The study provides data that will help healthcare providers, public health workers and community leaders identify where to focus their health improvement efforts for Chamorros and create culturally competent programs to promote health in this community