bluuebunny/crossref_metadata_2025 huggingface dataset:
Traumatic spinal cord injury in military personnel versus civilians: a propensity score-matched cohort study
Authors: Furlan Julio C , Kurban D , Craven B C
Published: June 2019

Introduction

Military personnel are exposed to mechanisms of bodily injuries that may differ from civilians. A retrospective cohort study (RCS) and a propensity score-matched cohort study (PSMCS) were undertaken to examine the potential differences in injury epidemiology, management and outcomes after spinal cord injury (SCI) between military personnel and civilians.

Methods

Using a Canadian multicentre SCI database, data of all individuals with sufficient data from October 2013 to January 2017 were included in the RCS (n=1043). In the PSMCS, a group of 50 military personnel with SCI was compared with a group of 50 civilians with SCI who were matched regarding sex, age, and level, severity and mechanism of SCI.

Results

In the RCS, military personnel with SCI (n=61) were significantly older and predominantl males when compared with civilians with SCI (n=982). However, the study groups were not statistically different with regards to their: level, severity and mechanisms of SCI; frequency of associated bodily injuries; and need for mechanical ventilation after SCI. In the PSMCS, the group of military individuals with SCI (n=50) was similar to the group of civilians with SCI (n=50) regarding pre-existing medical comorbidities, degree of motor impairment at admission, initial treatment for SCI and clinical and neurological outcomes after SCI.

Conclusions

The results of these studies suggest that military SCI group has disproportionally older men at the time of injury compared with civilians with SCI. However, the military and civilian SCI groups had similar outcomes of alike initial treatment when both groups were matched regarding their demographic profile and injury characteristics.
Prediction of intracranial hemorrhages after mechanical thrombectomy of basilar artery occlusion
Authors: Neuberger Ulf , Seker Fatih , Schönenberger Silvia , Nagel Simon , Ringleb Peter Arthur , Bendszus Martin , Pfaff Johannes Alex Rolf , Möhlenbruch Markus Alfred
Published: June 2019

Background

Mechanical thrombectomy (MT) achieves high recanalization rates in basilar artery occlusion (BAO). A severe complication of MT in BAO is intracranial hemorrhage (ICH). Yet, knowledge of risk factors for ICH after MT in BAO is limited.

Objective

To evaluate clinical and procedural parameters of patients treated with MT owing to BAO to identify potential risk factors for ICH—in particular, symptomatic ICH (sICH), and assess their clinical relevance.

Methods

We conducted a retrospective analysis of 101 consecutive patients presenting with BAO, who were treated with MT in our centre. Important clinical and procedural parameters were analysed as possible predictors for any ICH and sICH according to the Heidelberg Bleeding Classification using univariate tests and multivariate logistic regressions.

Results

ICH occurred in 25 (24.8%) patients, with a total of 7 (6.9%) developing sICH. Treatment with glycoprotein IIb/IIIa (GPIIb/IIIa) inhibitors was independently associated with any ICH (OR=24.67, 95% CI 4.90 to 124.03) and sICH (OR=7.08, 95% CI 1.36 to 36.78). Also, a longer onset-to-recanalization time increased the risk of both any ICH (OR=1.17, 95% CI 1.07 to 1.31) and sICH (OR=1.22, 95% CI 1.08 to 1.42). Higher serum glucose levels were associated with a higher incidence of any ICH (OR=1.39, 95% CI 1.06 to 1.85) and a higher risk of a fatal outcome (OR=1.03, 95% CI 1.01 to 1.05).

Conclusion

Administration of GPIIb/IIIa inhibitor during the course of MT of BAO was identified as an important risk factor in the development of any ICH and sICH.
John Michael Pemble
Authors: Pemble Simon
Published: June 2019

A lifelong practitioner whose interest in surgery and practice led him to get involved with the Society of Practising Veterinary Surgeons.
Early Rapid Decline in Kidney Function as a Beneficial Sign After Starting Antihypertensive Medication
Authors: Beige Joachim
Published: June 2019

See Article
Cooper et al
Influence of Long-Term Tillage and Residue Management on Soil Biota in Tropical Climate
Authors: Zhong Shuang , Zeng Huicai
Published: June 2019

A study was conducted during 2002–2018 to study the effects of conservation practice on soil biota properties in a sandy clay loam soil. Tillage treatments consisted of conventional tillage (CT), reduced tillage (RT) and no-tillage (NT). Residue treatments consisted of 0 residue
input (NR), 50% residue input (HR), 100% residue input (TR). Microbial biomass C (MBC), urease (UA), and dehydrogenase (DHA) obtained a mean of 29.7–41.2% higher in RT and NT than in CT. In comparison with NR, HR and TR increased the numbers of mesofauna and macrofauna, but decreased
the number of microfauna. The residue addition soils favored soil moisture, total organic C and available K. Soil fauna community analysis indicates that residue systems provide a more diverse residue resource entry into soil food webs, and that NT system is beneficial for soil ecosystem stability
and sustainability.
MTGase Mediated Polymerization of 11S Globulin to Enhance Its Functionalities and the Proteomics
Authors: Liu Ying , Dou Boxin , Fan Lili , Zhang Xiaolin , Liu Yumeng , Xu Jinli , Maclhujirh Terrence
Published: June 2019

In this paper, the effects of the induced polymerization and modification by microbial transglutaminase (MTGase) on solubility and emulsifiability of soybean 11S globulin was studied. The mechanism was also investigated through proteomics. Using SDS-PAGE and optical density scanning
to testify MTGase enzymatic polymerization, the optimum conditions showed that: temperature was 45 °C, ionic condition I was 0.2, pH was 8.0, the amount of enzyme was 30 U/g. The molecular weight of modified 11S globulin subunits concentrated at 15 kDa. The solublity and emulsifiability
were significantly enhanced. There were 268 differential protein spots extracted. Ten protein spots had significant differences in expression which totally focused on seed storage proteins, promoting seed germination and maturation proteins, proteins involved in osmotic regulation and alcohol
dehydrogenase, which affect solubility and emulsifiability of 11S globulin.
Effect of Straw Incorporation on Corn Yield in North China: A Meta-Analysis
Authors: Zhao Ben , Wang Xiaolong , Ata-Ul-Karim Syed Tahir , Liu Zhandong , Duan Aiwang
Published: June 2019

Crop straw incorporation (SI) is as an effective method of soil improvement, which is playing an increasingly important role in improving corn productivity, soil fertility and reducing nitrogen leaching and ammonia volatilization in North China. We conducted a meta-analysis to examine
the effect of crop SI on corn yield in North China, and to investigate how the effect varies with region, climate, soil, duration of crop SI, cropping system, and application of inorganic fertilizer (nitrogen, phosphorus, potassium). Results indicated that crop SI significantly increased corn
yield by 12.3% in North China. Crop SI had the most significant effect on improving corn yield under the annual precipitation ≤ 400 mm (18.6%) and mean temperature > 12 °C (15.4%) environment. The change rate of corn yield was higher with the experimental duration of 2–3 years
(13.3%) than that of ≥4 years (8.52%). Corn yield responses to crop SI in the corn–corn cropping system (10.42%) were not as obvious as that in the corn–wheat cropping system (14.73%). Therefore, we conclude that crop SI could significantly increase corn yield, which can reduce
environmental pollution and add farmer income.
Studies on Differential Proteins of Two Forms of Xenorhabdus Nematophilus
Authors: Zhang Fengqin , Guo Yajie , Liu Xiongduo , Liu Yong , Peng Liuliang , He Nongyue , Liu Tonghua , Li Xiaolong
Published: June 2019

As an entomopathogenic bacterium, Xenorhabdus nematophila (X. nematophila) can be parasitic in the gut of entomopathogenic nematodes. Compared with the primary form of X. nematophila, its secondary form has more differences in physiological characteristics and metabolic
pathways, such as insecticide, antibacterial, anti-cancer and other functions. Many studies about the application of X. nematophila as biological agents in ecological environment have been reported. However, its pathogenic mechanism and specific functional molecules remain to be studied.
Therefore, this study is dedicated to research differential proteins between the primary and the secondary bacteria with iTRAQ technique to further explore the functional mechanism and functional proteins of X. nematophila. A total number of 367 differentially expressed proteins were
detected with iTRAQ technique. Besides Xenocoumacin and Xnph1 proteins that had been widely studied by some scholars, other specific proteins in primary bacteria excavated are as follows: Xcn and Xnph1 proteins with antibacterial property, proteins related to the attachment of X. nematophila
to the host insects including flagellin, fimbrial adaptor, fimbrial adhesion and capsular synthesis regulator component B, glutamate synthase that plays an important role in multiple metabolic pathways in the primary bacteria, multidrug resistance secretion proteins with the property of tumor
drug resistance and defense effects against cytotoxic compounds produced by normal cells and malignant cells.
Using the Modified Sugarcane Bagasse Cellulose Cation Membrane as a Separator in the Electrodialysis Device for Ammonia Nitrogen Removal
Authors: Xiong Jianhua , Guo Shuocheng , Hou Yanping , Dai Zhao , Zhu Hongxiang , Wang Shuangfei , Chen Guoning
Published: June 2019

Bulk modification was applied to synthesize modified sugarcane bagasse cellulose (MSBC) cation exchange membrane using in electrodialysis device for the removal of ammonia nitrogen wastewater. The effect of MSBC mass fraction and pore forming agent in casting solution on membrane physico-chemical
properties was studied. XRD showed that the crystal lattice of cellulose in membrane transformed from cellulose I to cellulose II, and the FT-IR displayed that there were no new functional groups after the membrane formation. With the increase of MSBC, the moisture content and swelling degree
of the membrane decreased, but the mechanical strength increased and the IEC increased from 0.68 to 1.05 mmol/g. With the increase of pore-forming agent, the moisture content, swelling degree and increased in varying degrees, but the mechanical strength decreased, it is worth noting that ion
exchange capacity (IEC) has increased from 0.31 to 1.35 mmol/g as the amount of pore agent is four times that of MSBC. The influence of initial concentration of ammonia nitrogen wastewater, voltage and electrolysis time on the removal rate of ammonia nitrogen was investigated. The results
showed that the removal efficiency of ammonia nitrogen was higher when the initial concentration was 1500–2000 mg/L, and the removal rate of ammonia nitrogen can be up to 90.12% under the condition of the voltage is 10 V and the electrolysis time is 7 h. This implied that the cation
exchange membranes prepared from MSBC can be produced that can be appliedin wastewater treatment.
Photoprotective Effects of Ice Plant (Mesembryanthemum crystallinum) Callus Extract on Gene Expression of Human Dermal Fibroblast Against UV Exposure
Authors: Lin Yung-Hsiang , Lin Yung-Kai , Lin Yung-Hao
Published: June 2019

UV exposure is the principal cause of extrinsic photoaging. Antioxidant-related genes (SOD2 and CAT) and collagen-related genes (COL1A1 and TIMP1) were selected for analysis of the mRNA expression in human dermal fibroblasts (CCD-966SK) using qPCR. In this
study, UVA-exposed (15 J/cm2 cells showed decrease in SOD2 , CAT (p < 0 001) and COL1A1 (p < 0 05) gene expression, indicating the decline of antioxidant ability and collagen formation. However, treatment with ice plant callus extract (2 mg/mL,
24 h) before UVA exposure significantly up-regulated SOD2 , CAT , COL1A1 and TIMP1 genes (p < 0 01) by 9.5, 2.7, 1.7 and 3.8 times, respectively, compared with those of the control group, and by 10.2, 4.3 (p < 0 001), 2.1 and 3.8 times, respectively, compared
with those of the UVA (only) group. These results demonstrated that ice plant extracts affect both antioxidant- and collagen-related gene expressions, and show positive effects on endogenous antioxidant activity and skin collagen preservation.
Improved Stability of Baeyer–Villiger Mono-Oxygenase from Pseudomonas fluorescens by Substitution of Cysteine Residues
Authors: Liu Luo , Meng Shuaiqi , Wei Ren , Jiang Min , Wang Fang , Nie Kaili , Tan Tianwei
Published: June 2019

Baeyer–Villiger mono-oxygenases (BVMOs) are enzymes that could insert an oxygen into carbon skeletons adjacent to carbonyl groups to form lactone or ester. Recently, a new method of sebacic acid production from oleic acid based on Pseudomonas fluorescens DSM 50106 (PfBVMO)
catalysis was reported. However, the instable property of PfBVMO limited the further investigation and application of this enzyme. In this study, we aimed to improve the stability of PfBVMO by sitedirected mutagenesis. The sequence of PfBVMO indicates probable factors from the susceptible
oxidizing cysteine residue leading to the instability of PfBVMO. Activity and stability of mutants and the wild-type were characterized to investigate the correlation of cysteine residues and activity. The mutant enzyme with entire cysteine residues replaced by serine residues, showed significantly
higher stability than the wild type. The result would facilitate further work on the enzyme modification and crystal structure determination. Meanwhile, the results of this study provided potential for other enzyme stability evolution.
Ozonation of Coking Wastewater Effluent to Remove Disinfection By-Product Precursors Using an O3 Fluidized Bed Reactor
Authors: Lin Chong , Zhuo Xianrong , Guo Xiu , Yuan Mengyang
Published: June 2019

Various types of dissolved organic matter (DOM) exist in the effluent of coking wastewater treatment plants. These can become precursors for disinfection by-products (DBPs) during chlorination in downstream drinking water treatment plants, representing a significant threat to the water
supply. In this study, an O3 fluidized bed reactor (FBR) process is proposed to stabilize and reduce the number of precursors of haloacetonitriles (HANs) and trihalomethanes (THMs) in coking wastewater effluent. It was found that higher O3 concentrations resulted in lower
amounts of precursors. Analysis of dissolved organic carbon (DOC) and total organic carbon (TOC), as well as three-dimensional excitation-emission matrix (3D EEM) fluorescence spectroscopy, showed that O3 preferentially decomposed DOM containing unsaturated and aromatic components.
Ozonation also changed the species of organic compounds in the effluent. Furthermore, nitrogenous organic compounds such as nitrogen-containing heterocyclic compounds and nitrile were quickly oxidized. Amines were first increased then decreased, while the olefin and phenol, which contain unsaturated
functional groups, were also selectively oxidized by ozonation. Organic acids and alkanes were formed during ozonation; however, these intermediate compounds showed relatively low formation potential for HANs and THMs. Therefore, the precursors can be mineralized or converted to organic compounds,
which are less likely to form disinfection by-products. These results indicate that the ozonation treatment of coking wastewater effluent can effectively minimize disinfection by-product formation potential (DBPFP), reducing risks to the downstream water supply.
Endophytic Fungal Diversity on Different Aged Bark of Medicinal Taxus Plant Species
Authors: Fan Xiaokuan , Sarsaiya Surendra , Yu Jing , Pan Xiwu , Jin Leilei , Shi Jingshan , Chen Jishuang
Published: June 2019

The present study investigated the endophytic biodiversity on different aged Taxus bark using highthroughput sequencing. Fungi from the five Taxus species, including Taxus yunnanensis, T. media, T. chinensis, and two unidentified Taxus species
I and II, were identified by Internal Transcribed Spacer sequence analysis. The operational taxonomic units (OTUs) were 141, 114, 98, 67 and 36, respectively, while the genera numbers were 22, 14, 16, 9 and 10 from the species distribution data, respectively. The dominant fungal genera were
Mycena (94.33%), Phageophora (43.97%) and Paraconiothyrium (34.02%), followed by Pestalotiopsis (1.58%) and Strelitziana (1.15%). Rankabundance curve and diversity index suggested that the major species from the bark of T. yunnanensis (YN) was endophytic
fungi, whereas fungal species from the bark of unidentified Taxus species II (HDS 50) were quite rare. Furthermore, tree age notably influenced the bark endophytic fungal biodiversity.
Optimization and Kinetic Study of Biodiesel Production from Diseased Swine Carcasses
Authors: Jiao Youzhou , Mei Yahe , Wang Le , Liu Jiaao , Zhang Zhiping , Pan Xiaohui , Xu Guizhuan , He Chao
Published: June 2019

The innocuous utilization of diseased swine carcasses is a key issue in reducing environmental pollution and ensuring safety in animal husbandry. In this study, by using fat from diseased swine carcasses as raw materials, response surface experiments were conducted to investigate the
influences of reaction time, catalyst concentration, reaction temperature, and methanol/oil molar ratio on the biodiesel purity and the optimum conditions for biodiesel production were determined. Furthermore, three-dimensional (3D) ultrasound assistance was adopted and kinetic analysis was
performed. The results show that the influencing factors on biodiesel purity, in descending order, were determined to be reaction temperature > catalyst concentration > reaction time > methanol/oil molar ratio. Moreover, the maximum biodiesel purity was 93.7% under the following optimal
conditions: catalyst concentration of 5.0 wt%; reaction temperature of 68 °C; methanol/oil molar ratio of 10:1; reaction time of 37 h. When 3D ultrasound assistance was adopted, the maximum biodiesel purity of 98.1% was obtained for the reaction process of 8 h under the ultrasound power
and frequency of 500 W and 20 kHz, respectively. And the esterification reaction time was significantly reduced, compared to without ultrasound assistance. The results of kinetic analysis demonstrate that the reaction rate constants of the ultrasound group were 4.45–5.52 times greater
than that of the control group. And the activation energy for the ultrasound group was 25.58 kJ/mol, which is 22.81% lower than that of the control group. This study will help to conduct large-batch biodiesel production from diseased swine carcasses in the future.
Systematic Investigation of the Electrochemical Properties of Natural Melanin for Various Electrode Cells
Authors: Madkhali Nawal , Hassib Adel , Alqahtani Hadi R. , Al-Britain Hamad , Alterary Seham , Laref Amel
Published: June 2019

Melanin is an organic semiconductor from a broad class of functional macromolecules found in nature. It has hybrid properties of an amorphous semiconductor. However, natural melanin has not been widely studied; this is partially due to the lack of processing methods, as melanin is generally
extracted and measured as a powder. Thus, better electrical methods are required; one possible solution is the use of electrochemical synthesis. We investigated the electrochemical properties of melanin at different degrees of acidity and for various electrode cells. The electrochemical properties
of natural melanin were studied using two different techniques: the constant potential method and cyclic voltammetry. This could allow different types of charge transport and make melanin a contender in this rapidly growing field. Natural melanin is believed to be a hybrid conductor where
the dominant charge carriers change between electrons and protons as the acidity degree and water concentration change. Biocompatible and biodegradable features make melanin useful for electrochemical energy storage and it could be an attractive candidate for applications in bioelectronics
and electronics devices.
Epilepsy Seizure Detection Using Akima Spline Interpolation Based Ensemble Empirical Mode Kalman Filter Decomposition by EEG Signals
Authors: Baskar K. , Karthikeyan C.
Published: June 2019

Epilepsy is an incessant neurological disorder. The Epilepsy seizures are generated due to the aggravation in transient signals in Cerebrum. These seizures can be detected by analyzing the Electroencephalogram (EEG) Signals. The Akima Spline Interpolation based Ensemble Empirical Mode
Kalman Filter Decomposition (ASI-EEMKFD) model proposed in the paper focuses on detecting seizures automatically through a stable algorithm written in Python by using PyEEG package. The signal detection process is done in three phases. First, the EEG signals are acquired through data sets.
Then the signal is decomposed using Akima Spline interpolation for finding the intrinsic mode function. Further the signal is decomposed by implementing the steps involved in the Ensemble Empirical Mode Decomposition (EEMD). During the decomposition Kalman filter is used in order to remove
the white Gaussian noise. Finally, the decomposed signals are applied to the Long Term Short Term Memory (LTST) deep learning classifier which classifies the ictal, pre-ictal and healthy signal. Our proposed method produces the result higher compared with the existing EEMD Methods with the
accuracy rate of 98.2%, sensitivity of 94.96% and specificity of 93.72%.
Blending Ensemble of Fine-Tuned Convolutional Neural Networks Applied to Mammary Image Classification
Authors: Zhang Jingyi , Pan Shuwan , Hong Huichao , Kong Lingke
Published: June 2019

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent
on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network
(CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological
images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features
extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental
results are able to prove the effectiveness of our strategy.
High-Order Functional Connectivity Network for Differential Diagnosis of Depression
Authors: Yang Jian , Mao Xu , Liu Ning , Zhong Ning
Published: June 2019

Resting-state functional connectivity (FC) changes dynamically and major depressive disorder (MDD) has abnormality in functional connectivity networks (FCNs), but few existing resting-state fMRI study on MDD utilizes the dynamics, especially for identifying depressive individuals from
healthy controls. In this paper, we propose a methodological procedure for differential diagnosis of depression, called HN3D, which is based on high-order functional connectivity networks (HFCN). Firstly, HN3D extracts time series by independent component analysis, and partitions them into
overlapped short series by sliding time window. Secondly, it constructs a FCN for each time window and concatenates correlation matrices of all FCNs to generate correlation time series. Then, correlation time series are grouped into different clusters and high-order correlations for HFCN is
calculated based on their means. Finally, graph based features of HFCNs are extracted and selected for a linear discriminative classifier. Tested on 21 healthy controls and 20 MDD patients, HN3D achieved its best 100% classification accuracy, which is much higher than results based on stationary
FCNs. In addition, most discriminative components of HN3D locate in default mode network and visual network, which are consistent with existing stationary-based results on depression. Though HN3D needs to be studied further, it is helpful for the differential diagnosis of depression and might
have potentiality in identifying relevant biomarkers.
A Rapid and Convenient Fetal Heart Screening Proposal Based on Dynamic Sequential Cross-Section
Authors: Zhou Yi , Qiu Wenqian , Jiang Shuqing , He Danqing , Zhang Chaoxue
Published: June 2019

Congenital heart defects (CHDs) are a global health burden and a leading cause of infant morbidity and mortality. Fetal echocardiography is currently the best method for diagnosing CHDs prenatally, but it is not yet widely used for all fetuses because it is a time-consuming process
that requires a highly skilled sonographer. We propose a dynamic sequential cross-section analysis as a screening method for CHDs; this screening method can systematically evaluate the fetal heart effectively and quickly.
The Pilot Study of Spectral CT for the Diagnosis of Mediastinal Lymph Node Metastasis in Primary Lung Cancer
Authors: Chi Xiao-Tong , Bai Nian-Qiang , Li Ming-Ying , Deng Kai , Wang Guang-Li
Published: June 2019

Objective: To investigate the application value of spectral CT for the diagnosis of mediastinal lymph node metastasis in primary lung cancer. Materials and Methods: 32 patients who had primary lung cancer with 90 enlarged mediastinal lymph nodes underwent spectral CT by
using gemstone spectral imaging (GSI) mode. The spectral curves of regions of interest (ROI) in primary lung cancer and mediastinal lymph nodes were measured with GSI post-processing software. The spectral curve slope between 40 and 140 keV was calculated and compared between them for the
diagnosis of mediastinal lymph node metastasis. The data were divided into metastatic group (group A) and non-metastatic group (group B) according to the pathologic findings. SPSS17.0 software with paired sample t test was used for statistical analysis and P < 0.05 was considered
statistically significant difference. Results: The spectral curve slopes in primary lung cancer and mediastinal lymph node in group B were 0.96 ± 0.13 and 1.32 ± 0.17. There was significant difference between them (t = –2.68, P < 0.05). While there
was no significant difference between them in group A (t = –1.62, P > 0.05) with the values of 1.06 ± 0.09 and 1.09 ± 0.12. The accuracy, sensitivity and specificity of diagnosing mediastinal lymph node metastasis were 84.21%, 86.67% and 82.77%, respectively
in line with spectral curve. Conclusions: Spectral CT, as a new and non-invasive method, has valuable effect for the diagnosis of mediastinal lymph node metastasis in pre-operative staging of lung cancer.
Compressed Sensing MRI via Extended Anisotropic and Isotropic Total Variation
Authors: Zeng Fanfan , Du Hongwei , Jin Jiaquan , Xu Jinzhang , Qiu Bensheng
Published: June 2019

Compressed sensing (CS) is a technique to reconstruct images from undersampling data, reducing the scanning time of magnetic resonance imaging (MRI). It utilizes the sparsity of images in some transform domains. Total variation (TV) has been applied to enforce sparsity. However, traditional
TV based on the l1-norm is not the most direct way to induce sparsity, and it cannot offer a sufficiently sparse representation. Since the lp-norm (0< p < 1) promotes the sparsity better than that of the l1-norm, we
propose two extended TV algorithms based on the lp-norm: anisotropic and isotropic total p-variation (TpV). Then we introduce them to the MRI reconstruction model. We apply the Bregman iteration technique to handle the proposed optimization problem. During the
iteration, the p-shrinkage operator is employed to resolve the nonconvex problem caused by the lp-norm. Experimental results illustrate that our algorithms could offer the higher SNR and lower relative error compared with traditional TV algorithms and high-degree
TV (HDTV) algorithm in MRI reconstruction problem.
Quantum Optimization of Evolutionary Algorithm Using Droplet-Based Advanced Digital Microfluidic Biochip and Clinical Prognosis Configuration
Authors: Brindha G. , Rohini G.
Published: June 2019

In this research work M × N, DNA micro array is utilized with the help of Quantum optimization of evolutionary algorithm for accurate performance estimation in the system configuration provides high throughput using clinical prognosis application based on image processing.
In an existing system the droplet-manipulation method based on a "cross-referencing" method that is used for "row" and "columns" to access electrodes. In our research work proposed Advanced Digital Micro Fluidic Biochip (ADMFB) process is a synthesis configuration of linear way dynamic routing
segment used faster execution span related to the previous bio chip module. These techniques are minimizing the consumption of power and area. Experimental outputs shows the improvement in the static power, dynamic power and delay while comparing the previous research work and proposed research
work.
Study on Prognosis Factors of Non-Small Cell Lung Cancer Based on CT Image Features
Authors: Lu Xiaoteng , Gong Jing , Nie Shengdong
Published: June 2019

This study aims to investigate the prognosis factors of non-small cell lung cancer (NSCLC) based on CT image features and develop a new quantitative image feature prognosis approach using CT images. Firstly, lung tumors were segmented and images features were extracted. Secondly, the
Kaplan-Meier method was used to have a univariate survival analysis. A multiple survival analysis was carried out with the method of COX regression model. Thirdly, SMOTE algorithm was took to make the feature data balanced. Finally, classifiers based on WEKA were established to test the prognosis
ability of independent prognosis factors. Univariate analysis results reflected that six features had significant influence on patients' prognosis. After multivariate analysis, angular second moment, srhge and volume were significantly related to the survival situation of NSCLC patients (P
< 0.05). According to the results of classifiers, these three features could make a well prognosis on the NSCLC. The best classification accuracy was 78.4%. The results of our study suggested that angular second moment, srhge and volume were high potential independent prognosis factors
of NSCLC.
Establishment and Imaging Studies of Human Lung Cancer-Associated Brain Metastasis Animal Models
Authors: Li Jianbo , Zhang Guojian , Zhu Mingming , Wang Xuemei , Li Xiao-Feng
Published: June 2019

Background: Lung cancer is one of the malignant tumors with fast increase in morbidity and mortality, and great threat to human health and life. Methods: Animal models (n = 5) were performed by injecting 1 × 106 A549 cells in 0.1 mL phosphate buffer
saline into nude mice through the left ventricle. Body weight of animals was measured every 3 days, and changes in the appearance and behavior were observed. Brain magnetic resonance imaging of 5 animals were performed using T1, T1 enhancement and T2 scan at the 4th, 6th, and 7th weeks after
injected with tumor cells. Animals (n = 5) were sacrificed after the last magnetic resonance scan, and brain slices of the animals were prepared to conduct hematoxylin-eosin staining. Results: At the 4th weeks after inoculation of tumor cells, there was no obvious change in the
appearance and behavior of animals and there were no obvious tumor metastases in the brains from brain magnetic resonance scan. At the 6th weeks, body weight of all animals (n = 5), one of which appeared paralysis in hind limb, decreased obviously, and results from MR scans showed there
were tumor metastases in the brains of all animals. Results from brain magnetic resonance scan images of one animal at 4th, 6th, and 7th weeks showed tumor metastases appeared in the animal's brain and gradually grew bigger. Result from hematoxylin-eosin staining of brain slices showed multiple
tumor metastases, consistent with the results of magnetic resonance imaging. Conclusions: Human lung cancer-associated brain metastasis animal models was successfully established, and micro-magnetic resonance imaging is a sensitive, accurate and non-invasive method for the detection
of lung cancer brain metastases in mice through this study.
ObSecure Logging (OSLo): A Framework to Protect and Evaluate the Web Search Privacy in Health Care Domain
Authors: Ullah Mohib , Islam Muhammad Arshad , Khan Rafiullah , Aleem Muhammad , Iqbal Muhammad Azhar
Published: June 2019

Users around the world send queries to the Web Search Engine (WSE) to retrieve data from the Internet. Users usually take primary assistance relating to medical information from WSE via search queries. The search queries relating to diseases and treatment is contemplated to be the most
personal facts about the user. The search queries often contain identifiable information that can be linked back to the originator, which can compromise the privacy of a user. In this work, we are proposing a distributed privacy-preserving protocol (OSLo) that eliminates limitation in the
existing distributed privacy-preserving protocols and a framework, which evaluates the privacy of a user. The OSLo framework asses the local privacy relative to the group of users involved in forwarding query to the WSE and the profile privacy against the profiling of WSE. The privacy analysis
shows that the local privacy of a user directly depends on the size of the group and inversely on the number of compromised users. We have performed experiments to evaluate the profile privacy of a user using a privacy metric Profile Exposure Level. The OSLo is simulated with a subset of 1000
users of the AOL query log. The results show that OSLo performs better than the benchmark privacy-preserving protocol on the basis of privacy and delay. Additionally, results depict that the privacy of a user depends on the size of the group.
Comparison of Classifier Configurations for the Classification of Cervical Intraepithelial Neoplasia Using Acetic Acid Test Images
Authors: Liu Jun , Du Hongwei , Lu Han , Peng Yun , Li Ling , Zhang Yingchun
Published: June 2019

Cervical cancer represents a major cause of death for women. Automatic classification of cervical images from acetic acid test could serve as a promising screening tool for cervical cancer. Despite an increasing volume of studies on automatic classification of cervical images, reported
methods varied markedly in terms of features and classifiers used, and therefore the performance. The classification performance using different configurations of the classifier has not been well characterized. The objective of this study was to evaluate several frequently used features and
classifiers in acetic-acid cervical image based cervical intraepithelial neoplasia classification. Seven typically used color or texture-based features and four frequently used classifiers (Support Vector Machine, Random Forest, Back-Propagation Neural Network and K-Nearest Neighbors) were
included in the comparison based on a balanced large sample size including 175 CIN negative and 175 CIN positive patients. The results showed that the Support Vector Machine demonstrated the best classification accuracy when a subset of features was used. The finding of this study may provide
useful reference values to the development of an automatic cervical cancer screening tool.
Cross Correlation Statistical Study of Biomarker for Cerebral Infarction
Authors: Lu Hao , Mai Mingjie , Guan Min , Sugimoto Kazuo , Wu Shikai , Wang Fei , Hiwasa Takaki , Shen Si , Wang Hao
Published: June 2019

Cerebral infarction (CI) is the most common cerebrovascular disorder with high fatality and disability rates worldwide, and transient ischemic attack (TIA) is a warning of CI, and early diagnosis and intervention of TIA are very important for the prevention of CI. We screened a human
aortic endothelial cell cDNA library using serum from TIA patients to obtain lysosomal-associated membrane protein 1 (LAMP1) antigen. Amplified luminescent proximity homogeneous assay-linked immunosorbentassay (AlphaLISA) revealed that the antibody levels against LAMP1 were significantly higher
in patients with TIA or acute-phase CI (aCI) compared with healthy donors (HDs) (P < 0.01) by examined in three independent cohorts (77 and 158 in the TIA and acute aCI patient cohorts, respectively, and 122 in HD cohort used as normal control). Spearman correlation analysis demonstrated
that LAMP1-Abs levels were positively correlated with cigarette smoking habit. The serum antibody levels against LAMP1 could potentially serve as a useful biomarker for early detection of TIA or predicting of the onset of CI.
Security Assessment Model for Database Relational Designs
Authors: Alshammari Bandar M.
Published: June 2019

The increasing number of data breaches has led many organizations to focus on securing their IT infrastructures and application architectures. However, the main causes of many of the latest attacks are not associated with these two architectures. The damage caused by most of the recent
attacks could have been minimized if more attention was given to enhancing the security of all components of the database architecture. The existing enterprise database architecture frameworks do not consider this issue a priority; hence, it has received minimal attention. The enterprise database
architecture is the most important architecture because it is responsible for defining how all types of data, whether security-critical or not, are stored and accessed. This paper focuses on addressing the lack of a complete solution to help enterprise system architects to address the security
of their organizations from early stages. The novelty of this approach is that it specifies how to modify the required artifacts by the enterprise database architecture to address security-critical data. The approach also defines a number of security measurements that help enterprise architects
in measuring the security of the organization database based on those artifacts. These metrics are developed based on the results of a cybersecurity experiment conducted on 100 randomly selected open-source websites. The paper's contributions also consist of the definition of a number of security
refactoring rules that specify how to modify current enterprise databases to make them more secure. This paper uses an existing relational diagram for a health clinic database to illustrate the application of the model to an existing database. The validity and applicability of these metrics
and refactoring rules are proved using an experiment conducted on a number of security-related databases.
Echocardiographic Evaluation of Anterior Mitral Valve Aneurysm Without Infective Endocarditis
Authors: Yang Li , Wang Ju , Liu Weiwei , Wang Yueheng
Published: June 2019

Mitral valve aneurysm (MVA) is un-common and occurs most commonly as a complication of infective endocarditis. Early diagnosis and surgical treatment are of great importance. In this study, we reported a 30-year-old man with anterior MVA in the A2 scallop without concurrent infective
endocarditis, which was recognized by preoperative real-time three-dimensional transesophageal echocardiography (RT-3D-TEE) and confirmed at operation with mitral valve replacement. This study presented a rare condition, and valuated the potential application of RT-3D-TEE in MVA diagnosis.
Predicting Mental-Illness from Twitter Activity Using Activity Theory Based Context Ontology
Authors: Waheed Talha , Aslam Muhammad , Awais Muhammad
Published: June 2019

In this digital era, people are becoming more comfortable to express their sentiments on social media rather than sharing them in person. The analysis of these sentiments is a doorway to understand their mental-health status. The literature proposed linguistic features-based methods
for their automatic analysis to see if the user needs any psychological assistance. However, these methods analysed expressed sentiments as discrete and fragmented information, without considering the holistic context. For this, activity theory has been employed to explicate the context of
sentiment expression activity of a Twitter user. This contextual information of tweeting activity is utilised for better classifier design in order to predict mental-illness. During the first step, tweets of self-reported mentally-ill users are collected. Then, the contextual information from
Twitter user account and tweets is populated into activity theory-based context ontology. The activity components and their associated fields are used as a contextual feature-set for SVM training. The solution is tested by a prototype application, on tweets of self-reported mentally-ill users,
and the results of the proposed contextual features are compared with classic linguistic features. The proposed features increased the accuracy by 23.11%, which proves the hypothesis that the inclusion of context is valuable for sentiment analysis.
The Echocardiographic Analysis of Aortic Root Structure Based on Gaussian Process and Hidden Space Segmentation
Authors: Yedan Li , Kunjing Pang , Yong Jiang , Hao Wang
Published: June 2019

The significance of automatic measurement of transesophageal three-dimensional echocardiography in the selection of valve models for transcatheter aortic valve replacement (TAVR) was discussed. Selecting the imaginary TAVR valve model according to the aforementioned 2D-TTE, 2D-TEE,
3D-TEE esie valves' semi-automatic monitoring method and the monitoring data of the aortic valve ring obtained by the MDCT valve valve. The model selection criteria are based on the user manual taking SAPIEN 3 valves, Core Valve valves and Lotus valves as examples. The comparison of model
selection results is carried out by using hidden space segmentation of Gauss process. The same result as that of the MDCT valve can be achieved when the TAVR valve model is selected according to the automatic measurement result of theesie Valves 3D-TEE.
Analysis of Migraine Induced Monitoring Imaging Data by Multilayer Mixed Cluster Detection
Authors: Shi Min , Yang Dong Dong , Zhou Yuan , Zhao Huan , Fang Yu
Published: June 2019

The complex network of resting brain function was constructed by graph theory to study the difference of network topology between migraine patients and normal people. The complex network of brain function of the two groups was constructed respectively, and the average clustering coefficient,
characteristic path length, small cosmopolitan, homology, median centrality and other measurement parameters of the two groups of complex networks were calculated and compared. The multi-layer hybrid ensemble clustering detection is introduced for data analysis, and the edge connectivity of
consensus is optimized by modular analysis combined with hill climbing algorithm to improve the performance of the multi-layer hybrid ensemble clustering detection process driven by modularity. Conclusion: The abnormal areas of resting brain function network in migraine patients are
related to pain management, visual processing and sensory relay, the findings of this study are helpful to better explain the clinical symptoms of migraine.
Fetal Ultrasound Image Evaluation of Chromosomal Anomaly Detection and Classification Using Conditional Rooted Neural Network (CRNN)
Authors: Padmavathy S. , Suresh P.
Published: June 2019

In this work, a computerized scheme of Chromosomal anomaly recognition and classification of Chromosomal abnormality such as Trisomy (T) T-13, T-18 and T-21 (Patau, Edwards and Down syndrome) based on Conditionally Rooted neural network (CRNN) with wavelet Filter. CRNN is used to estimate
the chromosomal anomaly features separation from fetal provisions. The clear template of feature estimation from the first-trimester fetus of ultrasound images will be used to train the CRNN Neural Network. The software has successfully identified and classified the region of chromosomal anomaly.
The evaluations show that our CRNN technique can attain good denoising and classification performance in comparison with existing methods. In this experiment, the results indicate that our proposed method can detect and classify the trisomy factors measurement from the US image regions precisely
and robustly against speckle noise. The classification of Fetus US image datasets was done using CRNN classifier, and the accuracy of classification was found to be Highly efficient resolution for Chromosomal anomaly detection.
Social Media News Classification in Healthcare Communication
Authors: Majeed Fiaz , Asif Muhammad Waqas , Hassan Muhammad Awais , Abbas Syed Ali , Lali M. Ikramullah
Published: June 2019

The trend of news transmission is rapidly shifting from electronic media to social media. Currently, news channels in general, while health news channels specifically send health related news on social media sites. These news are beneficial for the patients, medical professionals and
the general public. A lot of health related data is available on the social media that may be used to extract significant information and present several predictions from it to assist physicians, patients and healthcare organizations for decision making. However, A little research is found
on health news data using machine learning approaches, thus in this paper, we have proposed a framework for the data collection, modeling, and visualization of the health related patterns. For the analysis, the tweets of 13 news channels are collected from the Twitter. The dataset holds approximately
28k tweets available under 280 hashtags. Furthermore, a comprehensive set of experiments are performed to extract patterns from the data. A comparative analysis is carried among the baseline method and four classification algorithms which include Naive Bayes (NB), Support Vector Machine (SVM),
Logistic Regression (LR), Decision Tree (J48). For the evaluation of the results, the standard measures accuracy, precision, recall and f-measure have been used. The results of the study are encouraging and better than the other studies of such kind.
The design and application of a web-based teacher evaluation system for STEM education
Authors: Wu Chia-Chun , Yang Cheng-Ying , Hwang Min-Shiang , Lin Mei-Yu
Published: June 2019

The quality of a teacher is the most important factor that affects students’ learning and achievement. The weak or incompetent teachers are a threat to the reputation of the profession and the quality of education that students receive. Recently, educators and policymakers are concerned on how to improve teacher quality. Many types of research have demonstrated that teacher evaluation could promote the teaching efficiency and teacher quality. In order to avoid fearing from the instructors, good teacher evaluation should have the following factors. (1) Authenticity: Participants in the teacher evaluation system cannot be impersonated and signatures cannot be forged. (2) Integrity: The results of teacher evaluation cannot be altered. (3) Security: The details of the process of teacher evaluation and information of participants should be kept as secure as possible. (4) Privacy: Sensitive information such as the participant's name must be protected. Based on the characteristics mentioned above, to design a high-quality evaluation system, this work proposes the teacher evaluation system by applying the cryptography, to develop a web-based system for STEM education. All the participants were those with the ability to operate the online evaluation system. Finally, the system could really provide a fair evaluation.