bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-fat diet exacerbates postoperative pain and inflammation in a sex-dependent manner
Authors: Song Zongbin , Xie Wenrui , Strong Judith A. , Berta Temugin , Ulrich-Lai Yvonne M. , Guo Qulian , Zhang Jun-Ming 
Published: April 2018

Abstract
Obesity is often associated with increased pain, but little is known about the effects of obesity and diet on postoperative pain. In this study, effects of diet and obesity were examined in the paw incision model, a preclinical model of postoperative pain. Long–Evans rats were fed high-fat diet (40% calories from butter fat) or low-fat normal chow. Male rats fed high-fat diet starting 6 weeks before incision (a diet previously shown to induce markers of obesity) had prolonged mechanical hypersensitivity and an overall increase in spontaneous pain in response to paw incision, compared with normal chow controls. Diet effects in females were minor. Removing high-fat diet for 2 weeks before incision reversed the diet effects on pain behaviors, although this was not enough time to reverse high-fat diet-induced weight gain. A shorter (1 week) exposure to high-fat diet before incision also increased pain behaviors in males, albeit to a lesser degree. The 6-week high-fat diet increased macrophage density as examined immunohistochemically in lumbar dorsal root ganglion even before paw incision, especially in males, and sensitized responses of peritoneal macrophages to lipopolysaccharide stimuli in vitro. The nerve regeneration marker growth-associated protein 43 (GAP43) in skin near the incision (day 4) was higher in the high-fat diet group, and wound healing was delayed. In summary, high-fat diet increased postoperative pain particularly in males, but some diet effects did not depend on weight gain. Even short-term dietary manipulations, that do not affect obesity, may enhance postoperative pain.
Assessing Information Needs Regarding Metabolic Syndrome Among Gynecological Cancer Survivors
Authors: Jang Insil , Kim Ji-Su , Kim Minhae , Lee Eunkyung 
Published: April 2018

Background
Cancer survivors have an increased risk of non–cancer-related deaths, particularly metabolic syndrome (MetS).

Objective
We aimed to assess knowledge deficits regarding metabolism-related diseases among gynecological cancer survivors and the preferred source of health information.

Methods
Using a mixed methods approach, 70 participants responded to a structured modified version of the MetS questionnaire. We conducted 28 semistructured interviews of gynecological cancer survivors with MetS. Responses were independently coded by 2 researchers, including MetS knowledge, behaviors for self-management, and preferred learning methods.

Results
Metabolic syndrome was diagnosed in 17% of the participants. More than 50% of the participants wanted to learn about MetS and requested a consultation with healthcare providers, 70% reported that they had heard of MetS, and 61.4% reported that they had MetS-related knowledge (correct answer rate by MetS-related component, ~50%). The level of MetS-related knowledge was poor in both the quantitative and qualitative data. Most of the participants defined MetS-related self-management health behaviors as regular eating and exercise in their own words. Participants mostly wanted exercise management (29% of the participants), followed by dietary life management (27.4%), stress management (17.4%), weight management (13.7%), definition and diagnostic methods of MetS (9.1%), and smoking and drinking management (3.3%). Participants wished to use a handbook in small groups or receive counseling by healthcare providers.

Conclusion
We observed poor awareness and knowledge level and the need for information regarding MetS among gynecological cancer survivors.

Implications for Practice
An educational handbook or counseling could effectively improve self-management of health-related behaviors.
Single-Center Long-Term Analysis of Combined Liver-Lung Transplant Outcomes
Authors: Freischlag Kyle William , Messina Julia , Ezekian Brian , Mulvihill Michael S. , Barbas Andrew , Berg Carl , Sudan Debra , Reynolds John , Hartwig Matthew , Knechtle Stuart 
Published: April 2018

Background
Combined lung-liver transplantation (LLT) applies 2 technically challenging transplants in 1 patient with severe 2-organ failure.

Methods
Institutional medical records and United Network for Organ Sharing database were queried for patients at our institution that underwent LLT from 2000 to 2016.

Results
Twelve LLTs were performed from 2000 to 2016 including 9 male and 3 female recipients with a median age of 28.36 years. Indications for lung transplantation were cystic fibrosis (8), idiopathic pulmonary fibrosis (3), and pulmonary fibrosis secondary to hepatopulmonary syndrome (1). Indications for liver transplantation were cystic fibrosis (8), alcoholic cirrhosis (1), idiopathic cirrhosis (2), and alpha-1 antitrypsin deficiency (1). Median forced expiratory volume in 1 second at transplant was 27.8% (±20.38%), and mean Model for End-Stage Liver Disease was 10.5 (±4.68). Median hospital stay was 44.5 days. Seventy-five percent of recipients had 1+ new infection during their transplant hospitalization. Patients experienced 0.68 incidences of acute rejection per year with a 41.7% (95% confidence interval, 21.3%-81.4%) probability of freedom from rejection in the first-year. Patient survival was 100% at 30 days, 91.6% at 1 year, and 71.3% at 3 years. At the time of analysis, 7 of 12 patients were alive, of whom 3 survived over 8 years post-LLT. Causes of death were primary liver graft failure (1), bronchiolitis obliterans syndrome (2), and solid tumor malignancies (2).

Conclusions
Our results indicate that LLT is associated with comparable survival to other LLT series and provides a granular assessment of infectious and rejection rates in this rare population.
An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy
Authors: Pfammatter Jesse A. , Maganti Rama K. , Jones Mathew V. 
Published: April 2018

Summary and KeywordsObjectiveManual detection of spike-wave discharges (SWDs) from EEG records is time intensive, costly, and subject to inconsistencies/biases. Additionally, manual scoring often omits information on SWD confidence/intensity which may be important for the investigation of mechanistic-based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of pre-selected events to establish a confidence-based, continuous-valued scoring.MethodsWe develop a support vector machine (SVM)-based algorithm for the automated detection of SWDs in the γ2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency- and amplitude-based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained a SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60 24-hour EEG records. We provide a detailed assessment of intra- and inter-rater scoring that demonstrates advantages of automated scoring.ResultsThe algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep.SignificanceWhile there are automated and semi-automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand ‘ground truth’ in SWD detection (i.e., the most reliable features agreed upon by humans that also correlate with objective physiological measures).Key Point BoxClinicians and researchers may benefit from an automated method of SWD detection that provides a framework for the quantitative description of SWDs and how they relate to other electrographic events.We present an algorithm for the automated, consistent, and rapid scoring of SWDs that assigns a confidence to detected events that is highly correlated with human scoring confidence.We characterize the human inter- and intra-rater consistency in the scoring of potential SWD events and compare them with the algorithm.Events along the scoring continuum generated by the algorithm are temporally and proportionately correlated with changes in spectral power bands relevant to behavioral states including sleep.
Azithromycin resistance through interspecific acquisition of an epistasis dependent efflux pump component and transcriptional regulator in Neisseria gonorrhoeae
Authors: Wadsworth Crista B. , Arnold Brian J. , Abdul Sater Mohamad R. , Grad Yonatan H. 
Published: April 2018

ABSTRACTMosaic interspecifically acquired alleles of the multiple transferable resistance (mtr) efflux pump operon correlate with reduced susceptibility to azithromycin in Neisseria gonorrhoeae in epidemiological studies. However, whether and how these alleles cause resistance is unclear. Here, we use population genomics, transformations, and transcriptional analyses to dissect the relationship between variant mtr alleles and azithromycin resistance. We find that the locus encompassing the mtrR transcriptional repressor and the mtrCDE pump is a hotspot of interspecific recombination introducing alleles from N. meningitidis and N. lactamica into N. gonorrhoeae, with multiple rare haplotypes in linkage disequilibrium at mtrD and the mtr promoter region. Transformations demonstrated that resistance is mediated through epistasis between these two loci and that the full length of the mosaic mtrD allele is required. Gene expression profiling revealed the mechanism of resistance in mosaics couples the novel mtrDalleles with promoter mutations enhancing expression of the pump. Overall, our results demonstrate that epistatic interactions at mtr gained from multiple Neisseria has contributed to azithromycin resistance in the gonococcal population.AUTHOR SUMMARYNeisseria gonorrhoeae is the sexually transmitted bacterial pathogen responsible for over 100 million cases of gonorrhea worldwide each year. The incidence of reduced susceptibility to the macrolide class antibiotic azithromycin has increased in the past decade; however, a large proportion of the genetic basis of resistance to this drug remains unexplained. Recently, resistance has been shown to be highly associated with mosaic alleles of the multiple transferable resistance (mtr) efflux pump, which have been gained via horizontal gene exchange with other Neisseria. However, if and how these alleles caused resistance was unknown. Here, we demonstrate that resistance has been gained through epistasis between mtrD and the mtr promoter region using evidence from both population genomics and experimental genetic manipulation. Epistasis also acts within the mtrD locus alone, requiring the full length of the gene for phenotypic resistance. Transcriptomic profiling indicates that the mechanism of resistance in mosaics is likely derived from both structural changes to mtrD, coupled with promoter mutations that result in regulatory changes to mtrCDE.
A trade-off between error and synchrony when using temporal codes
Authors: Peterson Erik J , Voytek Bradley 
Published: April 2018

Neural oscillations can improve the fidelity of neural coding by grouping action potentials into synchronous windows of activity but this same effect can interfere with coding when action potentials become “over-synchronized”. Diseases ranging from Parkinson’s to epilepsy suggest such over-synchronization can lead to pathological outcomes, but the precise boundary separating healthy from pathological synchrony remains an open theoretical problem. In this paper, we focus on measuring the costs of translating from an aperiodic code to a rhythmic one and use the errors introduced in this translation to predict the rise of pathological results. We study a simple model of entrainment featuring a pacemaker population coupled to biophysical neurons. This model shows that “error” in individual cells’ computations can be traded for population-level synchronization of spike-times. But in this model error and synchronization are not traded linearly, but nonlinearly. The bulk of synchronization happens early with relatively low error. To predict this phenomenon we conceive of “voltage budget analysis”, where small time windows of membrane voltage in single cells can be partitioned into “oscillatory” and “computational”‘ terms. By comparing these terms we discover a set of inequalities that align with an inflection point in the curve of measured errors. In particular, when the entrainment and computational voltage terms are equal, the error curve plateaus. We show this point serves as a reliable natural boundary to define pathological synchrony in neurons. We also derive optimal algorithms for exchanging computational error with population synchrony.New and Noteworthy. We establish exact conditions for when rhythmic entrainment of precise spike-times in a neural population will improve or harm it’s ability to communicate.
PP2C phosphatases promote autophagy by dephosphorylation of the Atg1 complex
Authors: Memisoglu Gonen , Eapen Vinay V. , Haber James E. 
Published: April 2018

ABSTRACTAutophagy is orchestrated by the Atg1-Atg13 complex in budding yeast. Under nutrient-rich conditions, Atg13 is maintained in a hyperphosphorylated state by TORC1 kinase. After nutrient starvation, Atg13 is dephosphorylated, triggering Atg1 kinase activity and autophagy induction. The phosphatases that dephosphorylate Atg13 remain uncharacterized. We show that two redundant PP2C phosphatases, Ptc2 and Ptc3, regulate autophagy via dephosphorylating both Atg13 and Atg1. In the absence of these phosphatases, starvation-induced macroautophagy is inhibited, as is the cytoplasm-to-vacuole targeting (Cvt) pathway, and the recruitment of the essential autophagy machinery to phagophore assembly sites (PAS) is impaired. Despite prolongation of the DNA damage-induced checkpoint in ptc2Δ ptc3Δ cells, genotoxin-induced autophagy is also blocked. Creating a genomic atg13-8SA allele under its endogenous promoter, lacking key TORC1 phosphorylation sites, bypasses the autophagy defect in ptc2Δ ptc3Δ strains. Taken together, these results imply that PP2C type phosphatases promote autophagy by regulating Atg1 complex.
Deep learning achieves super-resolution in fluorescence microscopy
Authors: Wang Hongda , Rivenson Yair , Jin Yiyin , Wei Zhensong , Gao Ronald , Günaydin Harun , Bentolila Laurent A. , Ozcan Aydogan 
Published: April 2018

AbtsractWe present a deep learning-based method for achieving super-resolution in fluorescence microscopy. This data-driven approach does not require any numerical models of the imaging process or the estimation of a point spread function, and is solely based on training a generative adversarial network, which statistically learns to transform low resolution input images into super-resolved ones. Using this method, we super-resolve wide-field images acquired with low numerical aperture objective lenses, matching the resolution that is acquired using high numerical aperture objectives. We also demonstrate that diffraction-limited confocal microscopy images can be transformed by the same framework into super-resolved fluorescence images, matching the image resolution acquired with a stimulated emission depletion (STED) microscope. The deep network rapidly outputs these super-resolution images, without any iterations or parameter search, and even works for types of samples that it was not trained for.
Human stem cell resources are an inroad to Neandertal DNA functions
Authors: Dannemann Michael , Vernot Benjamin , Pääbo Svante , Kelso Janet , Camp J. Gray 
Published: April 2018

ABSTRACTPluripotent stem cells from diverse humans offer the potential to study human functional variation in controlled culture environments. A portion of this variation originates from ancient admixture between modern humans and Neandertals, which introduced alleles that left a phenotypic legacy on individual humans today. Here we show that a large repository of human induced pluripotent stem cells (iPSCs) harbors extensive Neandertal DNA, including most known functionally relevant Neandertal alleles present in modern humans. This resource contains Neandertal DNA that contributes to human phenotypes and diseases, encodes hundreds of amino acid changes, and alters gene expression in specific tissues. Human iPSCs thus provide an opportunity to experimentally explore the Neandertal contribution to present-day phenotypes, and potentially study Neandertal traits.
YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU
Authors: Quang Daniel , Guan Yuanfang , Parker Stephen C.J. 
Published: April 2018

AbstractMotivationMotif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME.ResultsHere we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold.AvailabilityYAMDA is freely available on Github (https://github.com/daquang/YAMDA)Contactdaquang@umich.edu
Pharmacophore-guided discovery of CDC25 inhibitors causing cell cycle arrest and cell death
Authors: Kabakci Zeynep , Käppeli Simon , Cozza Giorgio , Cantù Claudio , König Christiane , Toggweiler Janine , Gentili Christian , Ribaudo Giovanni , Zagotto Giuseppe , Basler Konrad , Pinna Lorenzo A. , Ferrari Stefano 
Published: April 2018

ABSTRACTCDC25 phosphatases have a key role in cell cycle transitions and are important targets for cancer therapy. Here, we set out to discover novel CDC25 inhibitors. Using a combination of computational approaches we defined a minimal common pharmacophore in established CDC25 inhibitors and performed a virtual screening of a proprietary library. Taking advantage of the availability of crystal structures for CDC25A and CDC25B and using a molecular docking strategy, we carried out hit expansion/optimization. Enzymatic assays revealed that naphthoquinone scaffolds were the most promising CDC25 inhibitors among selected hits. At the molecular level, the compounds acted through a mixed-type mechanism of inhibition of phosphatase activity, involving reversible oxidation of cysteine residues. In 2D cell cultures, the compounds caused arrest of the cell cycle at the G1/S or at the G2/M transition. Mitotic markers analysis and time-lapse microscopy confirmed that CDK1 activity was impaired and that mitotic arrest was followed by death. Finally, studies on 3D organoids derived from intestinal crypt stem cells of Apc/K-Ras mice revealed that the compounds caused arrest of proliferation.
Automatic meeting summarization and topic detection system
Authors: Huang Tai-Chia , Hsieh Chia-Hsuan , Wang Hei-Chia 
Published: April 2018

PurposeProducing meeting documents requires an instantaneous recorder during meetings, which costs extra human resources and takes time to amend the file. However, a high-quality meeting document can enable users to recall the meeting content efficiently. The paper aims to discuss these issues.Design/methodology/approachAn application based on this framework is developed to help the users find topics and obtain summarizations of meeting contents without extra effort. This app uses the Bluemix speech recognizer to obtain speech transcripts. It then combines latent Dirichlet allocation and a TextTiling algorithm with the speech script of meetings to detect boundaries between different topics and evaluate the topics in each segment. TextTeaser, an open API based on a feature-based approach, is then used to summarize the speech transcripts.FindingsThe results indicate that the summaries generated by the machine are 85 percent similar to the records written by humankind.Originality/valueTo reduce the human effort in generating meeting reports, this paper presents a framework to record and analyze meeting contents automatically by voice recognition, topic detection, and extractive summarization.
The structural characteristics of innovation ecosystem: a fashion case
Authors: Lin Shuyu 
Published: April 2018

PurposeThe purpose of this paper is to provide empirical evidence of open innovation mechanism specific to aesthetic innovation. Prior research have drawn its research attention to technological innovation and focussed on the biopharmaceutical and technological sectors as the main research contexts. Thus, to gain a wider picture of the structural characteristics of innovation ecosystem, this paper aims to complement the existing technological-centred literature and provides understanding of aesthetic innovation by focussing on exploring the role of actors and intermediaries in either facilitating or inhibiting innovation in the creative sector.Design/methodology/approachThis is an exploratory study and the qualitative method was employed. With aim to explore aesthetic innovation in the creative sector, this research grounded its context in the UK designer fashion sector, which is part of the creative industries. Data were collected through a series of semi-structured interviews with respondents being purposely selected so as to ensure representativeness. A wider range of participants within the designer fashion sector was covered. Such multiple-sourced data collection approach allowed this research to triangulate data so that research trustworthiness was enhanced.FindingsThe result suggests the role of a broker in beyond traditionally facilitating knowledge exchange. This paper provides empirical evidence regarding the critical role of a broker in shaping the open innovation mechanism when it comes to aesthetic innovation. A broker in fact occupies a strategic position that allows it to negotiate relationships among the connected organisations and thus to govern the interaction mechanism of the innovation ecosystem as a whole. Given the empowered broker in such setting, the relationship between innovation creators and innovation seekers is controlled by a broker, which leads to risk that an innovation creator may not fully appropriate the value that it creates.Research limitations/implicationsThe main limitation of this study is its specific context, that is, the UK designer fashion industry. Such focus limits the scope of generalisation particularly its application to the entire creative sector. Thus, it is suggested that future research may consider to explore other creative industries so that the understanding of the open innovation mechanism from a structural perspective applied to aesthetic innovation can be enhanced.Originality/valueThis paper outlines the structural mechanism of open innovation applied to aesthetic innovation. The result contributes to the literature by complementing the findings derived from technology-centred research. Also, empirical evidence of this study is particularly of value to policy makers in the aspect of creative sector development. The suggested implications provide strategies of innovation ecosystem that could benefit individual actors involved in this open innovation setting and ensure prosper of the sector as a whole.