bluuebunny/crossref_metadata_2025_split huggingface dataset:
18F-FDG PET/CT and Bone Scintigraphy Findings in Tietze Syndrome
Authors: Oh Jeong Hee , Park Soo Bin , Oh Hong Chul 
Published: August 2018

Abstract
We present an interesting image of a 62-year-old woman revealed with Tietze syndrome by 18F-FDG PET/CT and bone scintigraphy. She presented with right upper chest wall pain with a hard, palpable mass. However, chest radiograph and CT were unrevealing. On PET/CT, intense FDG uptake was noted at the anterior aspect of the right second costal cartilage with dense calcification, which was the correct symptomatic lesion. Bone scan showed increased radioactive uptake at the FDG uptake lesion, but mild uptake was also seen in the asymptomatic lesion.
Nerve ultrasound and magnetic resonance imaging in the diagnosis of neuropathy
Authors: Goedee H. Stephan , van der Pol W. Ludo , Hendrikse Jeroen , van den Berg Leonard H. 
Published: August 2018

Purpose of review
This review summarizes the most relevant developments in the fields of nerve ultrasound and MRI in the diagnosis of treatable inflammatory neuropathies over the last 18 months.

Recent findings
MRI and nerve ultrasound can accurately identify potentially treatable neuropathies and thereby help to improve diagnosis. Advanced MRI techniques also show potential to dissect pathophysiology. The apparent mismatch between nerve function and morphology is not surprising and reflects different dimensions of the disease process in neuropathies.

Summary
MRI and nerve ultrasound have become useful tools in the diagnosis of inflammatory neuropathies.

Video Abstract

http://links.lww.com/CONR/A45
Neural circuit basis of aversive odour processing in Drosophila from sensory input to descending output
Authors: Huoviala Paavo , Dolan Michael-John , Love Fiona M. , Myers Philip , Frechter Shahar , Namiki Shigehiro , Pettersson Lovisa , Roberts Ruairí J.V. , Turnbull Robert , Mitrevica Zane , Breads Patrick , Schlegel Philipp , Bates Alexander Shakeel , Rodrigues Tiago , Aso Yoshinori , Bock Davi , Rubin Gerald M. , Stensmyr Marcus , Card Gwyneth , Costa Marta , Jefferis Gregory S.X.E. 
Published: August 2018

AbstractEvolution has shaped nervous systems to produce stereotyped behavioural responses to ethologically relevant stimuli. For example when laying eggs, female Drosophila avoid geosmin, an odorant produced by toxic moulds. Here we identify second, third, and fourth order neurons required for this innate olfactory aversion. Connectomics data place these neurons in a complete synaptic circuit from sensory input to descending output. We find multiple levels of valence-specific convergence, including a novel form of axo-axonic input onto second order neurons conveying another danger signal, the pheromone of parasitoid wasps. However, we also observe extensive divergence: second order geosmin neurons connect with a diverse array of 80 third order cell types. We find a pattern of convergence of aversive odour channels at this level. Crossing one more synaptic layer, we identified descending neurons critical for egg-laying aversion. Our data suggest a transition from a labelled line organisation in the periphery to a highly distributed central brain representation that is then coupled to distinct descending pathways.
A new insight into underlying disease mechanism through semi-parametric latent differential network model
Authors: He Yong , Ji Jiadong , Xie Lei , Zhang Xinsheng , Xue Fuzhong 
Published: August 2018

AbstractBackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.ResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.ConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.
LOTUS: a Single- and Multitask Machine Learning Algorithm for the Prediction of Cancer Driver Genes
Authors: Collier Olivier , Stoven Véronique , Vert Jean-Philippe 
Published: August 2018

AbstractCancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types.In this paper we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including informations about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types.We empirically show that LOTUS outperforms three other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.Author summaryCancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by targeted therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous informations into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction softwares.
Linking Fungal and Bacterial Proliferation to Microbiologically Influenced Corrosion in B20 Biodiesel Storage Tanks
Authors: Stamps Blake W. , Bojanowski Caitlin L. , Drake Carrie A. , Nunn Heather S. , Lloyd Pamela F. , Floyd James G. , Berberich Katelyn A. , Neal Abby R. , Crookes-Goodson Wendy J. , Stevenson Bradley S. 
Published: August 2018

ABSTRACTBiodiesel is a renewable substitute, or extender, for petroleum diesel that is composed of a mixture of fatty acid methyl esters (FAME) derived from plant and animal fats. Ultra-low sulfur diesel (ULSD) blended with up to 20% FAME can be used interchangeably with ULSD, is compatible with existing infrastructure, but is also more susceptible to biodegradation. Microbial proliferation and fuel degradation in biodiesel blends has not been directly linked in situ to microbiologically influenced corrosion. We, therefore, conducted a yearlong study of B20 storage tanks in operation at two locations, identified the microorganisms responsible for observed fuel fouling and degradation, and measured in situ corrosion. The bacterial populations were more diverse than the fungal populations, and largely unique to each location. The bacterial populations included members of the Acetobacteraceae, Clostridiaceae, and Proteobacteria. The abundant Eukaryotes at both locations consisted of the same taxa, including a filamentous fungus within the family Trichocomaceae, and the Saccharomycetaceae family of yeasts. Increases in the absolute and relative abundances of the Trichocomaceae were correlated with significant, visible fouling and pitting corrosion. This study identified the relationship between recurrent fouling of B20 with increased rates of corrosion, largely at the bottom of the sampled storage tanks.
Genomic signatures accompanying the dietary shift to phytophagy in polyphagan beetles
Authors: Seppey Mathieu , Ioannidis Panagiotis , Emerson Brent C. , Pitteloud Camille , Robinson-Rechavi Marc , Roux Julien , Escalona Hermes E. , McKenna Duane D. , Misof Bernhard , Shin Seunggwan , Zhou Xin , Waterhouse Robert M. , Alvarez Nadir 
Published: August 2018

AbstractBackgroundThe diversity and evolutionary success of beetles (Coleoptera) are proposed to be related to the diversity of plants on which they feed. Indeed the largest beetle suborder, Polyphaga, mostly includes plant-eaters among its ~315,000 species. In particular, plants defend themselves with a diversity of specialized toxic chemicals. These may impose selective pressures that drive genomic diversification and speciation in phytophagous beetles. However, evidence of changes in beetle gene repertoires driven by such interactions remains largely anecdotal and without explicit hypothesis testing.ResultsTo address this, we explored the genomic consequences of beetle-plant trophic interactions by performing comparative gene family analyses across 18 species representing the two most species-rich beetle suborders. We contrasted the gene contents of species from the mostly plant-eating suborder Polyphaga with those of the mainly predatory Adephaga. We found gene repertoire evolution to be more dynamic, with significantly more adaptive lineage-specific expansions, in the more speciose Polyphaga. Testing the specific hypothesis of adaptation to plant-feeding, we identified families of enzymes putatively involved in beetle-plant interactions that underwent adaptive expansions in Polyphaga. There was especially strong support for the selection hypothesis on large gene families for glutathione S-transferase and carboxylesterase detoxification enzymes.ConclusionsOur explicit modeling of the evolution of gene repertoires across 18 species identifies adaptive lineage-specific gene family expansions that accompany the dietary shift towards plants in beetles. These genomic signatures support the popular hypothesis of a key role for interactions with plant chemical defenses, and for plant-feeding in general, in driving beetle diversification.
High resolution evolutionary analysis of within-host hepatitis C virus infection
Authors: Raghwani Jayna , Wu Chieh-Hsi , Ho Cynthia K. Y. , de Jong Menno , Molenkamp Richard , Schinkel Janke , Pybus Oliver G. , Lythgoe Katrina A. 
Published: August 2018

ABSTRACTDespite the breakthroughs in the treatment of HCV infection in recent years, we have a limited understanding of how virus diversity generated within individuals impacts the evolution and spread of HCV variants at the population scale. Addressing this gap will be important for building models for molecular epidemiology, which can identify main sources of disease transmission and evaluate the risks of drug-resistance mutations emerging and disseminating in a population. Here, we have undertaken a high-resolution analysis of HCV within-host evolution from four individuals co-infected with HIV. Specifically, we used long-read, deep-sequenced data of the full-length HCV envelope glycoprotein, longitudinally sampled from acute to chronic HCV infection to investigate the underlying viral evolutionary dynamics. In three individuals we found strong statistical support for population structure maintaining within-host HCV genetic diversity. Furthermore, we found significant variation in rates of molecular evolution among different regions of the HCV envelope region, both within and between individuals. Lastly, we report the first estimate of the within-host population genetic rate of recombination for HCV (0.28 x 10-7recombinations per site per day; interquartile range: 0.13-1.05 x 10-7), which is two orders of magnitude lower than that estimated for HIV-1, and four orders of magnitude lower than the nucleotide substitution rate of the HCV envelope gene. Together, these observations indicate that population structure and strong genetic linkage shapes within-host HCV evolutionary dynamics. These results will guide the future investigation of potential HCV drug resistance adaptation during infection, and at the population scale.
ENRICH: a fast method to improve the quality of flexible macromolecular reconstructions
Authors: Kazemi M. , Sorzano C. O. S. , Des Georges A. , Carazo J. M. , Vargas J. 
Published: August 2018

AbstractCryo-electron microscopy using single particle analysis requires the computational averaging of thousands of projection images captured from identical macromolecules. However, macromolecules usually present some degree of flexibility showing different conformations. Computational approaches are then required to classify heterogeneous single particle images into homogeneous sets corresponding to different structural states. Nonetheless, sometimes the attainable resolution of reconstructions obtained from these smaller homogeneous sets is compromised because of reduced number of particles or lack of images at certain macromolecular orientations. In these situations, the current solution to improve map resolution is returning to the electron microscope and collect more data. In this work, we present a fast approach to partially overcome this limitation for heterogeneous data sets. Our method is based on deforming and then moving particles between different conformations using an optical flow approach. Particles are then merged into a unique conformation obtaining reconstructions with improved resolution, contrast and signal-to-noise ratio, then, partially circumventing many issues that impact obtaining high quality reconstructions from small data sets. We present experimental results that show clear improvements in the quality of obtained 3D maps, however, there are also limits to this approach, which we discuss in the manuscript.
Signatures of negative frequency dependent selection in colonisation factors and the evolution of a multi-drug resistant lineage of Escherichia coli
Authors: McNally Alan , Kallonen Teemu , Connor Christopher , Abudahab Khalil , Aanensen David M. , Horner Carolyne , Peacock Sharon J. , Parkhill Julian , Croucher Nicholas J. , Corander Jukka 
Published: August 2018

AbstractEscherichia coli is a major cause of bloodstream and urinary tract infections globally. The wide dissemination of multi-drug resistant (MDR) strains of extra-intestinal pathogenic E. coli (ExPEC) poses a rapidly increasing public health burden due to narrowed treatment options and increased risk of failure to clear an infection. Here, we present a detailed population genomic analysis of the ExPEC ST131 clone, in which we seek explanations for its success as an emerging pathogenic strain beyond the acquisition of antimicrobial resistance (AMR) genes. We show evidence for evolution towards separate ecological niches for the main clades of ST131 and differential evolution of anaerobic metabolism, key colonisation and virulence factors. We further demonstrate that negative frequency-dependent selection acting across accessory loci is a major mechanism that has shaped the population evolution of this pathogen.
Local membrane charge regulates ல2 adrenergic receptor coupling to Gi
Authors: Strohman M.J. , Maeda S. , Hilger D. , Masureel M. , Du Y. , Kobilka B.K. 
Published: August 2018

AbstractG protein coupled receptors (GPCRs) are transmembrane receptors that signal through heterotrimeric G proteins. Lipid modifications anchor G proteins to the plasma membrane; however, little is known about the effect of phospholipid composition on GPCR-G protein coupling. The β2adrenergic receptor (β2AR) signals through both Gsand Giin cardiac myocytes where studies suggest that Gisignaling may be cardioprotective. However, Gicoupling is much less efficient than Gscoupling in most cell-based and biochemical assays, making it difficult to study β2AR-Giinteractions. To investigate the role of phospholipid composition on Gsand Gicoupling, we reconstituted β2AR in detergent/lipid mixed micelles and found that negatively charged phospholipids (PS and PG) inhibit β2AR-Gi3coupling. Replacing negatively charged lipids with neutral lipids (PC or PE) facilitated the formation of a functional β2AR-Gi3interaction that activated Gi3. Ca2+, known to interact with negatively charged PS, facilitated β2AR-Gi3interaction in PS. Mutational analysis suggested that Ca2+interacts with the negatively charged EDGE motif on the carboxyl-terminal end of the αN helix of Gi3and coordinates an EDGE-PS interaction. These results were confirmed in β2AR reconstituted into nanodisc phospholipid bilayers. β2AR-Gi3interaction was favored in neutral lipids (PE and PC) over negatively charged lipids (PG and PS). In contrast, basal β2AR-Gsinteraction was favored in negatively charged lipids over neutral lipids. In negatively-charged lipids, Ca2+and Mg2+facilitated β2AR-Gi3interaction. Taken together, our observations suggest that local membrane charge modulates the interaction between β2AR and competing G protein subtypes.
LittleBrain: a gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings
Authors: Guell Xavier , Goncalves Mathias , Kaczmarzyk Jakub R , Gabrieli John DE , Schmahmann Jeremy D , Ghosh Satrajit S 
Published: August 2018

ABSTRACTGradient-based approaches to brain function have recently unmasked fundamental properties of brain organization. Diffusion map embedding analysis of resting-state fMRI data revealed a primary-to-transmodal axis of cerebral cortical macroscale functional organization. The same method was recently used to analyze resting-state data within the cerebellum, revealing for the first time a sensorimotor-fugal macroscale organization principle of cerebellar function. Cerebellar gradient 1 extended from motor to non-motor task-unfocused (default-mode network) areas, and cerebellar gradient 2 isolated task-focused processing regions. Here we present a freely available and easily accessible tool that applies this new knowledge to the topographical interpretation of cerebellar neuroimaging findings. LittleBrain generates scatterplots that illustrate the relationship between cerebellar data (e.g., volumetric patient study clusters, task activation maps, etc.) and cerebellar gradients 1 and 2. This novel method of data mapping provides alternative, gradual visualizations that complement discrete parcellation maps of cerebellar functional neuroanatomy. We present application examples to show that LittleBrain can also capture subtle, progressive aspects of cerebellar functional neuroanatomy that would be difficult to visualize using conventional mapping techniques. Download and use instructions can be found at https://xaviergp.github.io/littlebrain.
IPSE, a parasite-derived host immunomodulatory protein, is a promising therapeutic for hemorrhagic cystitis
Authors: Zee Rebecca S. , Mbanefo Evaristus C. , Le Loc H. , Pennington Luke F. , Odegaard Justin , Jardetzky Theodore S. , Alouffi Abdulaziz , Akinwale Jude , Falcone Franco H. , Hsieh Michael H. 
Published: August 2018

AbstractChemotherapy-induced hemorrhagic cystitis is characterized by bladder pain and voiding dysfunction caused by hemorrhage and inflammation. Of currently available therapies, prophylactic 2-mercaptoethanesulfonic acid (MESNA) has limited efficacy and cannot treat pre-existing lesions. Therefore, novel therapeutic options to treat hemorrhagic cystitis are needed. We previously reported that systemic administration of the Schistosomiasis haematobium-derived protein H-IPSEH06 (IL-4-inducing principle from Schistosoma mansonieggs), is superior to 3 doses of MESNA in alleviating hemorrhagic cystitis. Based on prior reports by others on S. mansoni IPSE and additional work by our group, we reasoned that H-IPSEH06 mediates its effects on hemorrhagic cystitis by binding IgE on basophils and inducing IL-4 expression, promoting urothelial proliferation, and translocating to the nucleus to modulate expression of genes implicated in relieving bladder dysfunction. We speculated that local bladder injection of the S. haematobium IPSE ortholog IPSEH03, hereafter called H-IPSEH03, might be more efficacious in preventing hemorrhagic cystitis compared to systemic administration of IPSEH06. We demonstrate herein that H-IPSEH03 is a promising therapeutic for the treatment of voiding dysfunction and bladder pain in hemorrhagic cystitis. Namely, it attenuates ifosfamide-induced increases in bladder wet weight in an IL-4-dependent fashion. H-IPSEH03 relieves hemorrhagic cystitis-associated allodynia. Finally, H-IPSEH03 drives increased urothelial cell proliferation. This indicates that IPSE induces bladder healing mechanisms, which suggests that it may be a novel non-opioid analgesic to treat bladder pain syndromes.
The deadly touch: protein denaturation at the water-air interface and how to prevent it
Authors: D’Imprima Edoardo , Floris Davide , Joppe Mirko , Sánchez Ricardo , Grininger Martin , Kühlbrandt Werner 
Published: August 2018

ABSTRACTElectron cryo-microscopy analyzes the structure of proteins and protein complexes in vitrified solution. Proteins tend to adsorb to the air-water interface in unsupported films of aqueous solution, which can result in partial or complete denaturation of the protein. We investigated the structure of yeast fatty acid synthase at the air-water interface by electron cryo-tomography and single-particle image processing. Around 90% of complexes adsorbed to the air-water interface are partly denatured. We show that the unfolded regions are those facing the air-water interface. Denaturation by contact with air may happen at any stage of specimen preparation. Denaturation at the air-water interface is completely avoided when the complex is plunge-frozen on a substrate of hydrophilized graphene.