bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long term headache duration is a factor predicting nonresponse to detoxification and advice in medication overuse headache
Authors: Caproni Stefano , Bianchi Elisa , Cupini Letizia M , Corbelli Ilenia , Beghi Ettore , Calabresi Paolo , Sarchielli Paola ,   SAMOHA Study Group
Published: June 2015

Abstract

Background
Medication overuse headache (MOH) is a very heterogeneous disorder for which a recommended treatment is not yet available. The purpose of this study was to investigate any possible roles of demographic and clinical characteristics of MOH patients that might predict a response to detoxification and advice with or without preventive treatment.

Findings
This ancillary study is part of the Sodium vAlproate in the treatment of Medication Overuse HeadAche (SAMOHA) study that randomized 88 MOH patients for 3-month treatment period with sodium valproate (VPA) (800 mg/day) or placebo after a 6-day outpatient detoxification regimen. Demographic and clinical characteristics obtained on patients from both study arms were analyzed to point out an association with the response to the treatment. While for patients from VPA arm no significant results were obtained, comparing responders to non-responders to detoxification and advice to withdraw from MOH, a significant difference in headache duration was observed. Specifically, the efficacy of such treatment resulted ineffective in headache lasting longer than 30 years.

Conclusions
Our findings suggest that the benefit from detoxification and advice can be excluded in MOH of long duration. Therefore, a preventive treatment is suggested particularly for these patients.
Contact heat evoked potentials and habituation measured interictally in migraineurs
Authors: Beese Lena Clara , Putzer Denise , Osada Nani , Evers Stefan , Marziniak Martin 
Published: June 2015

Abstract

Background
A lack of habituation of different evoked potential modalities in migraine patients in-between attacks has been suggested.

Methods
This study investigates cortical response after painful stimuli evaluated by contact heat evoked potentials (CHEPs) and quantitative sensory testing (QST) during the migraine-free interval. We enrolled 22 migraine patients and 22 healthy subjects.

Results
Cortical potentials after contact heat stimulation of the cheeks and the volar forearm at a temperature of 51°C showed significantly reduced A-δ-amplitudes in patients and healthy controls. When the subjects’ attention was drawn to an arithmetic task, a partial lack of habituation of amplitude could be seen in migraine patients. QST did not show any difference between migraineurs and controls.

Conclusion
Our findings can be primarily deemed to demonstrate that patients and healthy controls show significantly lower amplitudes while performing the calculation task. Without performing the calculation task we could not show the expected lack of habituation in migraineurs. Yet, while performing the calculation task our results partly suggest that hypothesis.
Antinociceptive and anti-inflammatory activities of Geranium bellum and its isolated compounds
Authors: Velázquez-González Claudia , Cariño-Cortés Raquel , Gayosso de Lucio Juan A , Ortiz Mario I , De la O Arciniega Minarda , Altamirano-Báez Diana A , Ángeles Luis Jiménez- , Bautista-Ávila Mirandeli 
Published: June 2015

Abstract

Background

Geranium bellum Rose, locally known as “Pata de león”, is a perennial plant distributed in the mountains of Hidalgo, Mexico. It is widely used in Mexican traditional medicine to treat fever, pain, and gastrointestinal disorders. To date, there are not published studies regarding the in vivo antinociceptive and anti-inflammatory potential of the acetone-aqueous extract from the aerial parts of G. bellum.

Methods
Antinociceptive effects of the acetone-aqueous G. bellum (AGB) extract and the isolated compounds were assessed using experimental pain models, including thermal nociception like hot plate test, and chemical nociception induced by intraperitoneal acetic acid or subplantar formalin injection in vivo. The anti-inflammatory properties of the extract were studied using systemic administration in carrageenan-induced paw edema.

Results
Intra-gastric administration of AGB (75, 150, and 300 mg/kg) showed a dose-dependent antinociceptive effect in intraperitoneal acetic acid (writhing), thermal nociception in CD1 mice, and subplantar formalin models, as well as anti-inflammatory effect in carrageenan- induced paw edema in Wistar rats. Geraniin and quercetin showed the highest antinociceptive activity in writhing test, whereas ellagic acid was the most active compound in the hot plate model.

Conclusion
These studies provide evidences that G. bellum shows antinociceptive and anti- inflammatory effects, and gives support to its use in treating pain in Mexican traditional medicine.
Enhancing oral bioavailability of quercetin using novel soluplus polymeric micelles
Authors: Dian Linghui , Yu Enjiang , Chen Xiaona , Wen Xinguo , Zhang Zhengzan , Qin Lingzhen , Wang Qingqing , Li Ge , Wu Chuanbin 
Published: June 2015

Abstract
To improve its poor aqueous solubility and stability, the potential chemotherapeutic drug quercetin was encapsulated in soluplus polymeric micelles by a modified film dispersion method. With the encapsulation efficiency over 90%, the quercetin-loaded polymeric micelles (Qu-PMs) with drug loading of 6.7% had a narrow size distribution around mean size of 79.00 ± 2.24 nm, suggesting the complete dispersibility of quercetin in water. X-ray diffraction (XRD) patterns illustrated that quercetin was in amorphous or molecular form within PMs. Fourier transform infrared spectroscopy (FTIR) indicated that quercetin formed intermolecular hydrogen bonding with carriers. An in vitro dialysis test showed the Qu-PMs possessed significant sustained-release property, and the formulation was stable for at least 6 months under accelerated conditions. The pharmacokinetic study in beagle dogs showed that absorption of quercetin after oral administration of Qu-PMs was improved significantly, with a half-life 2.19-fold longer and a relative oral bioavailability of 286% as compared to free quercetin. Therefore, these novel soluplus polymeric micelles can be applied to encapsulate various poorly water-soluble drugs towards a development of more applicable therapeutic formulations.
Synthesis of gallium nitride nanostructures by nitridation of electrochemically deposited gallium oxide on silicon substrate
Authors: Ghazali Norizzawati Mohd , Yasui Kanji , Hashim Abdul Manaf 
Published: June 2015

Abstract
Gallium nitride (GaN) nanostructures were successfully synthesized by the nitridation of the electrochemically deposited gallium oxide (Ga2O3) through the utilization of a so-called ammoniating process. Ga2O3 nanostructures were firstly deposited on Si substrate by a simple two-terminal electrochemical technique at a constant current density of 0.15 A/cm2 using a mixture of Ga2O3, HCl, NH4OH and H2O for 2 h. Then, the deposited Ga2O3 sample was ammoniated in a horizontal quartz tube single zone furnace at various ammoniating times and temperatures. The complete nitridation of Ga2O3 nanostructures at temperatures of 850°C and below was not observed even the ammoniating time was kept up to 45 min. After the ammoniating process at temperature of 900°C for 15 min, several prominent diffraction peaks correspond to hexagonal GaN (h-GaN) planes were detected, while no diffraction peak of Ga2O3 structure was detected, suggesting a complete transformation of Ga2O3 to GaN. Thus, temperature seems to be a key parameter in a nitridation process where the deoxidization rate of Ga2O3 to generate gaseous Ga2O increase with temperature. The growth mechanism for the transformation of Ga2O3 to GaN was proposed and discussed. It was found that a complete transformation can not be realized without a complete deoxidization of Ga2O3. A significant change of morphological structures takes place after a complete transformation of Ga2O3 to GaN where the original nanorod structures of Ga2O3 diminish, and a new nanowire-like GaN structures appear. These results show that the presented method seems to be promising in producing high-quality h-GaN nanostructures on Si.
Half metal in two-dimensional hexagonal organometallic framework
Authors: Hu Hao , Wang Zhengfei , Liu Feng 
Published: June 2015

Abstract
Two-dimensional (2D) hexagonal organometallic framework (HOMF) made of triphenyl-metal molecules bridged by metal atoms has been recently shown to exhibit exotic electronic properties, such as half-metallic and topological insulating states. Here, using first-principles calculations, we investigate systematically the structural, electronic, and magnetic properties of such HOMFs containing 3d transition metal (TM) series (Sc to Cu). Two types of structures are found for these HOMFs: a buckled structure for those made of TMs with less half-filled 3d band and a twisted structure otherwise. The HOMFs show both ferromagnetic and antiferromagnetic properties, as well as nonmagnetic properties, due to the electronic configuration of the TM atoms. The V, Mn, and Fe lattices are ferromagnetic half metals with a large band gap of more than 1.5 eV in the insulating spin channel, making them potential 2D materials for spintronics application.
Enhanced performance of nitride-based ultraviolet vertical-injection light-emitting diodes by non-insulation current blocking layer and textured surface
Authors: Chiang Yen Chih , Lin Bing Cheng , Chen Kuo Ju , Lin Chien Chung , Lee Po Tsung , Kuo Hao Chung 
Published: June 2015

Abstract
For the purpose of light extraction and efficiency enhancement, the nitride-based ultraviolet vertical-injection light-emitting diodes (UV-VLEDs) with non-insulation current blocking layer (n-CBL) and optimized textured surface were fabricated. The optical and electrical characteristics were investigated in this n-CBL UV-VLED. Furthermore, the efficiency of optimized structure was improved by 5 ~ 6 times compared to our reference.
Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset
Authors: Shi Zhenguo , McLernon Des , Ghogho Mounir , Wu Zhilu 
Published: June 2015

Abstract
Spectrum sensing is an important aspect of an (interweave) cognitive radio network. In the particular case of orthogonal frequency division multiplexing (OFDM) transmission, many previous spectrum sensing algorithms have utilized the unique correlation properties provided by the cyclic prefix (CP). However, they have also had to both estimate and compensate for the inherent timing offset of a practical system. This is because the timing offset will affect both the test statistic and the threshold, and the inaccurate estimation of timing offset will lead to poor performance. So in this paper, we propose an improved CP detector by constructing a likelihood ratio test (LRT) based on the multivariate probability density functions (pdf) of a particular auto-correlation vector that is chosen to exploit the existence of the CP. This leads to ‘probability of detection’ (P

d
) and ‘probability of false alarm’ (P

f
) terms that are actually independent of timing offset, and we can get an accurate threshold without estimating timing offset. Simulation results illustrate that the proposed algorithm outperforms existing methods, even for low SNR values. Finally, we show how the algorithm’s parameters must be carefully chosen in a trade-off between spectrum sensing success and overall system performance.
MIMO channel capacity using antenna selection and water pouring
Authors: Cuan-Cortes Jose V , Vargas-Rosales Cesar , Munoz-Rodriguez David 
Published: June 2015

Abstract
The use of multiple-input multiple-output (MIMO) communication systems has attracted considerable attention due to capacity and performance improvements without increasing the required bandwidth or transmission power. Although MIMO improves the transmission rate and provides reliable communication, these advantages come at a high cost, since multiple radio frequency (RF) chains have to be employed. Therefore, cost-effective implementation of MIMO systems remains as an important challenge. In this sense, antenna selection techniques (AST) can help reduce the high cost of MIMO systems, while retaining most of their benefits; and in order to improve channel capacity, transmission power can be allocated efficiently according to the channel experienced by each antenna element. In this paper, we show the channel capacity improvement of MIMO systems, by combining the use of AST and transmission power allocation through the water-pouring algorithm (WPA) for different antenna configurations. We apply WPA and allocate more power to the channel with better conditions, when the MIMO channel is Rayleigh distributed and noise is Gaussian. By employing these two techniques (AST and WPA), we show that channel capacity is significantly improved, with higher capacity values than those obtained using traditional systems which just spread equal power among all the transmit antennas. We also show that bounds on the capacity can be considered when these improvements are used in a MIMO system.
A simultaneous iterative method for split equality problems of two finitefamilies of strictly pseudononspreading mappings without prior knowledge ofoperator norms
Authors: Che Haitao , Li Meixia 
Published: June 2015

AbstractIn this article, we first introduce the concept of T-mapping of a finitefamily of strictly pseudononspreading mapping "Equation missing", and we show that the fixed point set of theT-mapping is the set of common fixed points of"Equation missing" and T is a quasi-nonexpansive mapping.Based on the concept of a T-mapping, we propose a simultaneousiterative algorithm to solve the split equality problem with a way of selectingthe stepsizes which does not need any prior information about the operatornorms. The sequences generated by the algorithm weakly converge to a solution ofthe split equality problem of two finite families of strictly pseudononspreadingmappings. Furthermore, we apply our iterative algorithms to some convex andnonlinear problems.MSC: 47H09, 47H05, 47H06, 47J25.
Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works
Authors: Lamberti Fabrizio , Sanna Andrea , Paravati Gianluca 
Published: June 2015

AbstractThe automatic extraction of objective features from paintings, like brushstroke distribution, orientation, and shape, could be particularly useful for different artwork analyses and management tasks. In fact, these features contribute to provide a unique signature of the artists’ style and can be effectively used for artist identification and classification, artwork examination and retrieval, etc. In this paper, an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh’s paintings is presented. Through the iterative application of segmentation, characterization, and validation steps, valid brushstrokes complying with specific area and shape constraints are identified. On the extracted brushstrokes, several representative features can then be calculated, like orientation, length, and width. The accuracy of the devised method is evaluated by comparing numerical results obtained on a dataset of digital reproductions of van Gogh’s oil-on-canvas works with observations made by human subjects and with another recent approach for automatic brushstroke analysis. Experimental tests showed that the devised methodology produces results that are rather close to those obtained by human subjects and, for some of the metrics considered, can provide improved performances with respect to alternative techniques.
Spinal Autophagy is Differently Modulated in Distinct Mouse Models of Neuropathic Pain
Authors: Berliocchi Laura , Maiarù Maria , Varano Giuseppe Pasquale , Russo Rossella , Corasaniti Maria Tiziana , Bagetta Giacinto , Tassorelli Cristina 
Published: June 2015

Background Autophagy is a homeostatic degradative process essential for basal turnover of long-lived proteins and organelles as well as for removal of dysfunctional cellular components. Dysregulation of the autophagic machinery has been recently associated to several conditions including neurodegenerative diseases and cancer, but only very few studies have investigated its role in pain processing. Results We previously described autophagy impairment at the spinal cord in the experimental model of neuropathic pain induced by spinal nerve ligation (SNL). In this study, we characterized the main autophagic markers in two other common experimental models of neuropathic pain, the chronic constriction injury (CCI) and the spared nerve injury (SNI). The different modulation of LC3-I, Beclin 1 and p62 suggested that autophagy is differentially affected in the spinal dorsal horn depending on the type of peripheral injury. Confocal analysis of p62 distribution in the spinal dorsal horn indicated its presence mainly in NeuN-positive cell bodies and occasionally in glial processes, thus suggesting a predominant expression in the neuronal compartment. Finally, we investigated the consequences of autophagy impairment on pain behaviour by using the autophagy blocker cloroquine. Intrathecal chloroquine injection in naïve mice induced spinal accumulation of LC3 and p62 paralleled by significant mechanical hypersensitivity thus confirming the block in autophagosome clearance and suggesting the participation of the autophagic process in spinal mechanisms of pain processing. Altogether, our data indicate that spinal autophagy is differentially altered in different experimental pain models of neuropathic pain and that this process may be relevant for pain control.
A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature
Authors: Xu Shuo , An Xin , Zhu Lijun , Zhang Yunliang , Zhang Haodong 
Published: June 2015

Abstract

Background
In order to improve information access on chemical compounds and drugs (chemical entities) described in text repositories, it is very crucial to be able to identify chemical entity mentions (CEMs) automatically within text. The CHEMDNER challenge in BioCreative IV was specially designed to promote the implementation of corresponding systems that are able to detect mentions of chemical compounds and drugs, which has two subtasks: CDI (Chemical Document Indexing) and CEM.

Results
Our system processing pipeline consists of three major components: pre-processing (sentence detection, tokenization), recognition (CRF-based approach), and post-processing (rule-based approach and format conversion). In our post-challenge system, the cost parameter in CRF model was optimized by 10-fold cross validation with grid search, and word representations feature induced by Brown clustering method was introduced. For the CEM subtask, our official runs were ranked in top position by obtaining maximum 88.79% precision, 69.08% recall and 77.70% balanced F-measure, which were improved further to 88.43% precision, 76.48% recall and 82.02% balanced F-measure in our post-challenge system.

Conclusions
In our system, instead of extracting a CEM as a whole, we regarded it as a sequence labeling problem. Though our current system has much room for improvement, our system is valuable in showing that the performance in term of balanced F-measure can be improved largely by utilizing large amounts of relatively inexpensive un-annotated PubMed abstracts and optimizing the cost parameter in CRF model. From our practice and lessons, if one directly utilizes some open-source natural language processing (NLP) toolkits, such as OpenNLP, Standford CoreNLP, false positive (FP) rate may be very high. It is better to develop some additional rules to minimize the FP rate if one does not want to re-train the related models. Our CEM recognition system is available at: http://www.SciTeMiner.org/XuShuo/Demo/CEM.
CheNER: a tool for the identification of chemical entities and their classes in biomedical literature
Authors: Usié Anabel , Cruz Joaquim , Comas Jorge , Solsona Francesc , Alves Rui 
Published: June 2015

Abstract

Background
Small chemical molecules regulate biological processes at the molecular level. Those molecules are often involved in causing or treating pathological states. Automatically identifying such molecules in biomedical text is difficult due to both, the diverse morphology of chemical names and the alternative types of nomenclature that are simultaneously used to describe them. To address these issues, the last BioCreAtIvE challenge proposed a CHEMDNER task, which is a Named Entity Recognition (NER) challenge that aims at labelling different types of chemical names in biomedical text.

Methods
To address this challenge we tested various approaches to recognizing chemical entities in biomedical documents. These approaches range from linear Conditional Random Fields (CRFs) to a combination of CRFs with regular expression and dictionary matching, followed by a post-processing step to tag those chemical names in a corpus of Medline abstracts. We named our best performing systems CheNER.

Results
We evaluate the performance of the various approaches using the F-score statistics. Higher F-scores indicate better performance. The highest F-score we obtain in identifying unique chemical entities is 72.88%. The highest F-score we obtain in identifying all chemical entities is 73.07%. We also evaluate the F-Score of combining our system with ChemSpot, and find an increase from 72.88% to 73.83%.

Conclusions
CheNER presents a valid alternative for automated annotation of chemical entities in biomedical documents. In addition, CheNER may be used to derive new features to train newer methods for tagging chemical entities. CheNER can be downloaded from http://metres.udl.cat and included in text annotation pipelines.
CHEMDNER system with mixed conditional random fields and multi-scale word clustering
Authors: Lu Yanan , Ji Donghong , Yao Xiaoyuan , Wei Xiaomei , Liang Xiaohui 
Published: June 2015

Abstract

Background
The chemical compound and drug name recognition plays an important role in chemical text mining, and it is the basis for automatic relation extraction and event identification in chemical information processing. So a high-performance named entity recognition system for chemical compound and drug names is necessary.

Methods
We developed a CHEMDNER system based on mixed conditional random fields (CRF) with word clustering for chemical compound and drug name recognition. For the word clustering, we used Brown's hierarchical algorithm and Skip-gram model based on deep learning with massive PubMed articles including titles and abstracts.

Results
This system achieved the highest F-score of 88.20% for the CDI task and the second highest F-score of 87.11% for the CEM task in BioCreative IV. The performance was further improved by multi-scale clustering based on deep learning, achieving the F-score of 88.71% for CDI and 88.06% for CEM.

Conclusions
The mixed CRF model represents both the internal complexity and external contexts of the entities, and the model is integrated with word clustering to capture domain knowledge with PubMed articles including titles and abstracts. The domain knowledge helps to ensure the performance of the entity recognition, even without fine-grained linguistic features and manually designed rules.