bluuebunny/crossref_metadata_2025_split huggingface dataset:
COPD treatment choices based on blood eosinophils: are we there yet?
Authors: Oliver Brian , Tonga Katrina , Darley David , Rutting Sandra , Zhang Xin , Chen Hui , Wang Gang 
Published: December 2019

Eosinophils are increasingly being recognised as an important characteristic feature of COPD. Patients with COPD and eosinophilic inflammation tend to respond to steroid therapy; however, many questions remain regarding the optimum measurement. Eosinophilic inflammation may be defined based on various sampling techniques, including eosinophil levels in blood, sputum, bronchoalveolar lavage or biopsy, which leads to inconsistencies in its definition. Blood eosinophils may increase in conjunction with sputum eosinophils during COPD exacerbations and therefore may be a good surrogate marker of airway eosinophilic inflammation. However, the timing of the blood eosinophil measurement, the stability of the eosinophil count and the threshold used in different studies are variable. The use of blood eosinophil count to direct biological therapies in COPD has also had variable outcomes. Eosinophilic inflammation has an important role in COPD management; however, its use as the optimum biomarker still needs further investigation.Key pointsEosinophilia may play a significant role in the pathogenesis of COPD.Eosinophilic inflammation in COPD can be steroid responsive; however, eosinophilic inflammation is variable, and caution needs to be taken with measurements and the thresholds used.The long-term effects of reducing eosinophil levels in COPD is unclear.Educational aimsTo explore current knowledge of eosinophils in COPD.To explore the relationship between eosinophilia and corticosteroid use.To understand the limitations of assessing and using eosinophilia in COPD.
A comparative study of the microstructure and water permeability between flattened bamboo and bamboo culm
Authors: Chen Hong , Zhang Yutian , Yang Xue , Ji Han , Zhong Tuhua , Wang Ge 
Published: December 2019

AbstractThe objective of this study is to investigate the microstructure, water permeability and the adhesion of waterborne coating on the flattened bamboo. The flattened bamboo was obtained by softening bamboo culm at 180 °C followed by compression. The microstructure and chemical component of flattened bamboo were investigated by scanning electron microscopy, Fourier transforms infrared spectroscopy, and X-ray diffraction. The adhesion and interface structure of waterborne coating onto flattened bamboo surface were also examined. The result indicated that the parenchyma cells in flattened bamboo were compressed, and starch in the parenchyma cell was extracted during the softening and flattening process in which the main chemical component did not change significantly. The water permeability of both flattened bamboo and bamboo culm is dependent on the direction: longitudinal direction > tangential direction > radial direction. However, the water permeability in all three directions in flattened bamboo was higher than those in the untreated bamboo. In addition, alkali dye solution was found to more easily permeate through the flattened bamboo when compared to acid dye solution, and the permeability varied depending on alkali dye or acid dye concentration. The adhesion of water-based polyurethane coating on the flattened bamboo can reach the second level.
Investigation of Band Alignment for Hybrid 2D-MoS2/3D-β-Ga2O3 Heterojunctions with Nitridation
Authors: Huan Ya-Wei , Xu Ke , Liu Wen-Jun , Zhang Hao , Golosov Dmitriy Anatolyevich , Xia Chang-Tai , Yu Hong-Yu , Wu Xiao-Han , Sun Qing-Qing , Ding Shi-Jin 
Published: December 2019

AbstractHybrid heterojunctions based on two-dimensional (2D) and conventional three-dimensional (3D) materials provide a promising way toward nanoelectronic devices with engineered features. In this work, we investigated the band alignment of a mixed-dimensional heterojunction composed of transferred MoS2 on β-Ga2O3($$ 2- $$2-01) with and without nitridation. The conduction and valence band offsets for unnitrided 2D-MoS2/3D-β-Ga2O3 heterojunction were determined to be respectively 0.43 ± 0.1 and 2.87 ± 0.1 eV. For the nitrided heterojunction, the conduction and valence band offsets were deduced to 0.68 ± 0.1 and 2.62 ± 0.1 eV, respectively. The modified band alignment could result from the dipole formed by charge transfer across the heterojunction interface. The effect of nitridation on the band alignments between group III oxides and transition metal dichalcogenides will supply feasible technical routes for designing their heterojunction-based electronic and optoelectronic devices.
Development of a 1550-nm InAs/GaAs Quantum Dot Saturable Absorber Mirror with a Short-Period Superlattice Capping Structure Towards Femtosecond Fiber Laser Applications
Authors: Jiang Cheng , Ning Jiqiang , Li Xiaohui , Wang Xu , Zhang Ziyang 
Published: December 2019

AbstractLow-dimensional III–V InAs/GaAs quantum dots (QDs) have been successfully applied to semiconductor saturable absorber mirrors (SESAMs) working at a 900–1310-nm wavelength range for ultrafast pulsed laser applications benefitting from their broad bandwidth, wavelength flexibility, and low saturation fluence. However, it is very challenging to obtain a high-performance QD-SESAM working at the longer wavelength range around 1550 nm due to the huge obstacle to epitaxy growth of the QD structures. In this work, for the first time, it is revealed that, the InAs/GaAs QD system designed for the 1550-nm light emission range, the very weak carrier relaxation process from the capping layers (CLs) to QDs is mainly responsible for the poor emission performance, according to which we have developed a short-period superlattice (In0.20Ga0.80As/In0.30Ga0.70As)5 as the CL for the QDs and has realized ~ 10 times stronger emission at 1550 nm compared with the conventional InGaAs CL. Based on the developed QD structure, high-performance QD-SESAMs have been successfully achieved, exhibiting a very small saturation intensity of 13.7 MW/cm2 and a large nonlinear modulation depth of 1.6 %, simultaneously, which enables the construction of a 1550-nm femtosecond mode-locked fiber lasers with excellent long-term working stability.
ZIF-67-Derived CoSe/NC Composites as Anode Materials for Lithium-Ion Batteries
Authors: Li Zongyang , Zhang Lian Ying , Zhang Lei , Huang Jiamu , Liu Hongdong 
Published: December 2019

AbstractAs a typical metal selenide, CoSe is a kind of foreground anode material for lithium-ion batteries (LIBs) because of its two-dimensional layer structure, good electrical conductivity, and high theoretical capacity. In this work, the original CoSe/N-doped carbon (CoSe/NC) composites were synthesized using ZIF-67 as a precursor, in which the CoSe nanoparticles are encapsulated in NC nanolayers and they are connected through C–Se bonds. The coating structure and strong chemical coupling make the NC nanolayers could better effectively enhance the lithium storage properties of CoSe/NC composites. As a consequence, the CoSe/NC composites deliver a reversible capacity of 310.11 mAh g−1 after 500 cycles at 1.0 A g−1. Besides, the CoSe/NC composites show a distinct incremental behavior of capacity.
Identification of genes for salt tolerance and yield-related traits in rice plants grown hydroponically and under saline field conditions by genome-wide association study
Authors: Liu Chen , Chen Kai , Zhao Xiuqin , Wang Xiaoqian , Shen Congcong , Zhu Yajun , Dai Mingli , Qiu Xianjin , Yang Rongwei , Xing Danying , Pang Yunlong , Xu Jianlong 
Published: December 2019

Abstract
Background
Soil salinity is one of the main environmental conditions that affects rice production. Identifying the genetic loci that affect rice salt tolerance (ST)-related traits at the seedling stage, especially under saline field conditions, is crucial for ST rice breeding by pyramiding ST genes that act at different developmental stages.

Results
Large phenotypic variations were observed in 708 rice accessions, and yield and its related traits were considerably limited when exposed to salt stress. In a genome-wide association study (GWAS), 2255 marker-trait association signals were detected for all measured traits, and the significant SNPs were distributed in 903 genes. Of these, 43 genes processed same functional annotation, and the gene ontology terms “biological processes” and “molecular function” with the known genes responsive to salt stress in rice. Further haplotype analysis detected 15 promising candidates significantly associated with the target traits, including five known genes and 10 novel genes. We identified seven accessions carrying favorable haplotypes of four genes significantly associated with grain yield that performed well under saline stress conditions.

Conclusions
Using high density SNPs within genes to conduct GWAS is an effective way to identify candidate genes for salt tolerance in rice. Five known genes (OsMYB6, OsGAMYB, OsHKT1;4, OsCTR3, and OsSUT1) and two newly identified genes (LOC_Os02g49700, LOC_Os03g28300) significantly associated with grain yield and its related traits under saline stress conditions were identified. These promising candidates provide valuable resources for validating potential ST-related genes and will facilitate rice breeding for salt tolerance through marker-assisted selection.
Cell-Wide Survey of Amide-Bonded Lysine Modifications by Using Deacetylase CobB
Authors: Wei Yun , Yang Wan-Jie , Wang Qi-Jun , Lin Peng-Cheng , Zhao Jian-Yuan , Xu Wei , Zhao Shi-Min , He Xia-Di 
Published: December 2019

AbstractBackgroundLysine post-translational modifications are important regulators of protein function. Proteomic and biochemical approaches have resulted in identification of several lysine modifications, including acetylation, crotonylation, and succinylation. Here, we developed an approach for surveying amide-bonded lysine modifications in the proteome of human tissues/cells based on the observation that many lysine modifications are amide-bonded and that theSalmonella entericadeacetylase, CobB, is an amidase.ResultsAfter the proteome of human tissues/cells was denatured and the non-covalently bonded metabolites were removed by acetone washes, and the amide-bonded modifiers were released by CobB and analyzed using liquid- and/or gas chromatography/mass spectrometry metabolomic analysis. This protocol, which required 3–4 days for completion, was used to qualitatively identify more than 40 documented and unreported lysine modifications from the human proteome and to quantitatively analyze dynamic changes in targeted amide-bonded lysine modifications.ConclusionsWe developed a method that was capable of monitoring and quantifying amide-bonded lysine modifications in cells of different origins.
A hybrid deep learning framework for bacterial named entity recognition with domain features
Authors: Li Xusheng , Fu Chengcheng , Zhong Ran , Zhong Duo , He Tingting , Jiang Xingpeng 
Published: December 2019

Abstract
Background
Microbes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions with certain experimental evidences have been reported in biomedical literature. Integrating this knowledge into a database or knowledge graph could accelerate the progress of biomedical research. A crucial and necessary step in interaction extraction (IE) is named entity recognition (NER). However, due to the specificity of bacterial naming, there are still challenges in bacterial named entity recognition.

Results
In this paper, we propose a novel method for bacterial named entity recognition, which integrates domain features into a deep learning framework combining bidirectional long short-term memory network and convolutional neural network. When domain features are not added, F1-measure of the model achieves 89.14%. After part-of-speech (POS) features and dictionary features are added, F1-measure of the model achieves 89.7%. Hence, our model achieves an advanced performance in bacterial NER with the domain features.

Conclusions
We propose an efficient method for bacterial named entity recognition which combines domain features and deep learning models. Compared with the previous methods, the effect of our model has been improved. At the same time, the process of complex manual extraction and feature design are significantly reduced.
Multimodal deep representation learning for protein interaction identification and protein family classification
Authors: Zhang Da , Kabuka Mansur 
Published: December 2019

AbstractBackgroundProtein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge.ResultsIn this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods.ConclusionTo the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.
Virtual Grid Engine: a simulated grid engine environment for large-scale supercomputers
Authors: Ito Satoshi , Yadome Masaaki , Nishiki Tatsuo , Ishiduki Shigeru , Inoue Hikaru , Yamaguchi Rui , Miyano Satoru 
Published: December 2019

Abstract
Background
Supercomputers have become indispensable infrastructures in science and industries. In particular, most state-of-the-art scientific results utilize massively parallel supercomputers ranked in TOP500. However, their use is still limited in the bioinformatics field due to the fundamental fact that the asynchronous parallel processing service of Grid Engine is not provided on them. To encourage the use of massively parallel supercomputers in bioinformatics, we developed middleware called Virtual Grid Engine, which enables software pipelines to automatically perform their tasks as MPI programs.

Result
We conducted basic tests to check the time required to assign jobs to workers by VGE. The results showed that the overhead of the employed algorithm was 246 microseconds and our software can manage thousands of jobs smoothly on the K computer. We also tried a practical test in the bioinformatics field. This test included two tasks, the split and BWA alignment of input FASTQ data. 25,055 nodes (2,000,440 cores) were used for this calculation and accomplished it in three hours.

Conclusion
We considered that there were four important requirements for this kind of software, non-privilege server program, multiple job handling, dependency control, and usability. We carefully designed and checked all requirements. And this software fulfilled all the requirements and achieved good performance in a large scale analysis.
Association of household secondhand smoke exposure and mortality risk in patients with heart failure
Authors: He Xin , Zhao Jingjing , He Jiangui , Dong Yugang , Liu Chen 
Published: December 2019

Abstract
Background
Secondhand smoke (SHS) exposure is a well-established cardiovascular risk factor, yet association between SHS and prognosis of heart failure remains uncertain.

Method
Data were obtained from the US National Health and Nutrition Examination Surveys III from 1988 to 1994. Currently nonsmoking adults with a self-reported history of heart failure were included. Household SHS exposure was assessed by questionnaire. Participants were followed up through December 31, 2011. Cox proportional-hazards models were used to assess the association of household SHS exposure and mortality risk. Potential confounding factors were adjusted.

Results
Of 572 currently nonsmoking patients with heart failure, 88 were exposed to household SHS while 484 were not. There were totally 475 deaths during follow-up. In univariate analysis, household SHS was not associated with mortality risk (hazard ratio [HR]: 0.98, 95% confidence interval [CI]: 0.76–1.26, p = 0.864). However, after adjustment for demographic variables, socioeconomic variables and medication, heart failure patients in exposed group had a 43% increase of mortality risk compared with those in unexposed group (HR: 1.43, 95% CI: 1.10–1.86, p = 0.007). Analysis with further adjustment for general health status and comorbidities yielded similar result (HR: 1.47, 95% CI: 1.13–1.92, p = 0.005).

Conclusion
Household SHS exposure was associated with increased mortality risk in heart failure patients.
Detecting delirium in patients with acute stroke: a systematic review of test accuracy
Authors: Mansutti Irene , Saiani Luisa , Palese Alvisa 
Published: December 2019

Abstract
Background
Patients with acute stroke are particularly vulnerable to delirium episodes. Although delirium detection is important, no evidence-based recommendations have been established to date on how these patients should be routinely screened for delirium or which tool should be used for this purpose in this population. Therefore, the aim of this study was to identify delirium screening tools for patients with acute stroke and to summarise their accuracy.

Methods
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search of Medline, CINAHL and Scopus databases was performed to include: (a) diagnostic test accuracy studies; (b) evaluating tools detecting delirium among patients with acute stroke; (c) written in English; (d) published up to September 2018. The included studies were assessed in their quality by using the Quality Assessment of Diagnostic Accuracy Studies-2.

Results
A total of four studies have been performed to date in the field with a variable quality for the methodology used and documentation of the accuracy of mainly two tools, as (1) the 4-Assessment Test for delirium (4AT), reporting a range of sensitivity from 90.2 to 100% and a specificity from 64.5 to 86%; and (2) the Confusion Assessment Method-Intensive Care Unit (CAM-ICU) showing a sensitivity of 76% (95% Confidence of Interval [CI] 55–91) and a specificity of 98% (95%CI 93–100). Other tools have been studied as: The Abbreviated Mental Test-10, the Abbreviated Mental Test short form, the Clock Drawing Test, the Cognitive Examination derived from the National Institutes of Health Stroke Scale and the Glasgow Coma Scale. Moreover, the use of a single question—namely, ‘Does this patient have cognitive issues?’ as answered by the multidisciplinary team—has been subjected to a validation process.

Conclusions
To date a few primary studies have been published to test the accuracy of tools in their ability to detect post-stroke delirium; among those available, the 4AT and the CAM-ICU tools have been mostly studied. Research has just started to add evidence to the challenge of detecting and usefully assessing newly-acquired delirium among stroke patients: therefore, more studies are needed to improve the knowledge and allow a robust selection of the most useful tool to use in this population.
Baseline circulating stem-like cells predict survival in patients with metastatic breast Cancer
Authors: Lee Chun-Hui , Hsieh Jason Chia-Hsun , Wu Tyler Min-Hsien , Yeh Ting-Shiuan , Wang Hung-Ming , Lin Yung-Chan , Chen Jen-Shi , Lee Chia-Lin , Huang Wen-Kuan , Hung Tsung-Min , Yen Tzu-Tsen , Chan Sheng-Chieh , Chou Wen-Chi , Kuan Feng-Che , Hu Ching-Chih , Chang Pei-Hung 
Published: December 2019

Abstract
Background
Circulating tumor cells (CTCs) are associated with breast cancer prognosis. Research is limited regarding the role of circulating cancer stem-like cells (cCSCs) considering the treatment response and survival among patients with metastatic breast cancer. Accordingly, we performed this prospective study to clarify the prognostic significance of baseline cCSCs for metastatic breast cancer in terms of first-line chemotherapy.

Methods
Between April 2014 and January 2016, we prospectively enrolled 48 patients with stage IV breast invasive ductal carcinoma who underwent first-line chemotherapy. We identified and analyzed CTCs and cCSCs by using a protocol based on negative selection and flow cytometry before chemotherapy. CTCs were identified as EpCAM+Hoechst+CD45– cells and cCSCs as CD133+EpCAM+Hoechst+CD45– cells. cCSCs were expressed as a percentage of CTCs. The associations between CTCs, cCSCs, and the clinicopathological variables that were predictive of the treatment response and survival outcome were analyzed using univariate and multivariate analyses.

Results
We identified CTCs in all the enrolled patients, with a median number of 33.9/mL CTCs. CSCs were isolated in 97.9% of the patients; the median percentage of cCSCs was 14.7%. A high baseline level of cCSCs was correlated with an inferior tumor response rate (54.2% vs. 95.8%, p < 0.001), overall survival (OS; median: 27.7 months vs. not reached, p < 0.001), and progression-free survival (PFS; median: 5.7 vs. 18.0 months, p < 0.001). Multivariate analysis revealed that along with other clinical variables, baseline cCSCs remained an independent prognostic factor for OS and PFS.

Conclusions
Baseline cCSCs predict the treatment response as well as survival in patients with metastatic breast cancer undergoing first-line chemotherapy. Therefore, the measurement of cCSCs may assist in identifying early cancer treatment response and prognosis.