bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Dose X-ray CT Reconstruction Algorithm Using Shearlet Sparse Regularization
Authors: Xiao Dayu , Zhang Xiaotong , Li Jianhua , Bao Nan , Kang Yan
Published: December 2019

Computed tomography (CT) scans produce ionizing radiation in the body, and high-dose CT scans may increase the risk of cancer. Therefore, reducing the CT radiation dose is particularly important in clinical diagnosis, which is achieved mainly by reducing projection views and tube current.
However, the projection data are incomplete in the case of sparse views, which may cause stripe artifacts in the image reconstructed by the filtered back projection (FBP) algorithm, thereby losing the details of the image. Low current intensity also increases the noise of the projection data,
degrading the quality of the reconstructed image. This study aimed to use the alternating direction method of multipliers (ADMM) to address the shearlet-based sparse regularization problem, which is subsequently referred to as ADMM-shearlet method. The low-dose projection data were simulated
by adding Gaussian noise with zero mean to high-dose projection data. Then FBP, simultaneous algebraic reconstruction technique, total variation, and ADMM-shearlet methods were used to reconstruct images. Normalized mean square error, peak signal-to-noise ratio, and universal quality index
were used to evaluate the performance of different reconstruction algorithms. Compared with the traditional reconstruction algorithms, the ADMM-shearlet algorithm performed well in suppressing the noise due to the low dose while maintaining the image details.
An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
Authors: Liang Jingxian , Huang Jialin , Mu Liwei , Yu Baoxian , Chen Pengbin , Pang Zhiqiang , Nie Ruihua , Zhang Han
Published: December 2019

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat
intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak
and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed
method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.
Studies on Pulse Wave Model Based on Multiple Gaussian Decomposition
Authors: Yang Lin , Yang Jinzhong , Zhang Song , Meng Yu , Yang Yimin , Li Xuwen , Hao Dongmei , Shao Jing
Published: December 2019

The characteristics of pulse waveform reflect the physiological and pathological state of the human beings. However, it faces a lack of a comprehensive and detailed understanding. In this pursuit, the present research envisages to propose a new method of pulse waveform analysis based
on the traditional method of pulse wave analysis. In this study, the data of 932 subjects were studied that included the pulse wave of the finger vein volume and the pulse wave of the radial artery pressure. The pulse wave was re-analyzed in combination with the physiological process of the
heart. Pulse wave data processing was done using the Gaussian function decomposition fitting method to make up for the lost information in the pulse wave. The performance evaluation of this method was compared with the traditional pulse wave fitting. It was found that the difference of k
value of pulse wave after normalization was increased by 0.1%, the absolute value of average residuals was increased, and the variance of all data increased by three times. This method could fully reflect the characteristics of the pulse wave, and was found to be an effective characteristic
analysis method of pulse wave, thereby providing a scientific basis for the comprehensive analysis of the characteristics of the whole cardiac cycle pulse wave.
Predicting Alzheimer's Disease Based on Network Topological Latent Representations
Authors: Sun Jing , Ding Yanhui , Zhao Kun , Xu Haiyan , Zhang Yongxin , Gao Baozhong
Published: December 2019

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Right now there is no cure for AD. As we know, the brain structure is constantly changing as it progresses to AD. If we can detect these changes, it may be helpful to discover new biomarkers and early diagnosis of
AD. We use the deep learning method DeepWalk to extract the structural features of each subject based on brain function connection. The method takes a brain topology network based on functional connection transformations as input and outputs a potential feature representation for each brain
region in the brain network structure. In the current research, a new potential biomarker is found, which is based on a structural feature that can represent changes in brain connectivity. In our experiment, 79 Resting-state fMRI were used from ADNI dataset, including 49 normal controls (NCs)
and 30 AD patients. By analyzing the structural features between AD patients and NCs, obvious differences are detected in the temporal lobe, parietal lobe, and frontal lobe. We use a support vector machine (SVM) model to evaluate the performance of these structural features, and its classification
performance achieves 80.36% accuracy (specificity = 73.67%, sensitivity = 87.17%). In general, these findings indicate that structural features may be a new potential biomarker between NCs and AD patients.
A Modified Convolutional Neural Network for Resting-State EEG-Based Schizophrenia Classification with Weighted Electrodes
Authors: Ma Danyang , Yang Genke , Li Zeya , Liu Haichun , Pan Changchun , Li Lanzhen , Zhang Tianhong
Published: December 2019

Schizophrenia is a severe mental disorder that can result in hallucinations, delusions, and extremely disordered thinking and behavior. While electroencephalography (EEG) has been used as an auxiliary tool for diagnostic purposes in several recent studies, all EEG channels are treated
homogeneously without addressing the dominance of certain channels. The main purpose of this study is to obtain the weight value of each channel as the quantitative representation of influence of each scalp area on the classification of schizophrenia phases, and then to apply the weight values
to improve the accuracy of classification. We propose a new convolutional neural network (CNN) structure based on AlexNet to derive weight values as weight layer and classify the samples better. Our results show that the modified CNN structure achieves better performance in terms of time consumption
and classification accuracy compared with the original classifier. Also, the visualization of the weight layer in our model indicates possible correlations between scalp areas and schizophrenia conditions, which may benefit future pathological study.
Anatomical Skull-Stripping Template and Improved Boundary-Oriented Quantitative Segmentation Evaluation Metrics
Authors: Wang Shiqing , Shi Yonghong , Zhuang Han , Qin Chunli , Li Wensheng
Published: December 2019

Skull-removal is a standard preprocessing step for brain magnetic resonance image analysis. The gold standard of skull dissection are the anatomical templates of brain region after the manual removal of skull by experts. Manual segmentation is very time-consuming, but it is the evaluation
basis of automatic brain extraction method. The existing skull stripping templates have some confused and unclear partitions around the brain marginal regions (sulci, gyrus) and skull base (brainstem). To this end, the study carried out three tasks: First, we proposed a brain extraction protocol
that strictly followed human brain anatomy. Secondly, a more accurate skull stripping template was constructed on two open access datasets. Thirdly, we improved the commonly used segmentation evaluation metrics, such as Dice, Jaccard, sensitivity and precision, and proposed quantitative boundary-oriented
evaluation indicators that accurately reflect the vulnerable error segmentation of sulci, gyrus and brainstem. The proposed brain template is widely compared with the existing segmentation template and various automatic brain extraction results.
A New-Style Random Forest Diagnosis Model for Alzheimer's Disease
Authors: Sun Haijing , Wang Anna , Ai Qing , Wang Yang
Published: December 2019

In this paper, a new algorithm named cost sensitive and random forest -weighted algorithm (αCSRF) is proposed as a diagnostic model for Alzheimer's disease (AD). In the new algorithm, cost-sensitive learning is introduced into random forest algorithm, and weighted sum terms
of information gain ratio and misclassification cost decline ratio are constructed. In this model, [18F] AV1451 Tau-PET imaging data of areas of interest in the brain were selected as biological markers to classify disease course into three categories: normal control (NC), mild cognitive impairment
(MCI) and AD. Experiment proved that this model is a dynamic model that can calculate the misclassification cost and the classification accuracy by adjusting the parameters. According to the actual requirements, the weighting parameters can be selected to obtain a model with better comprehensive
performance. In this experiment, when the parameter is 0.6, the total error cost of the misclassification is quantified to 46.9, and the accuracy is 81.6%, which is the optimal comprehensive performance. Compared with other methods, the algorithm (αCSRF) proposed in this paper
is more flexible, more practical and more robust.
A Novel Fuzzy Rough Nearest Neighbors Emotion Recognition Approach Based on Multimodal Wearable Biosensor Network
Authors: Zheng Shupeng , Peng Chenhui , Fang Fang , Liu Xiaochu
Published: December 2019

Due to the diversity and complexity of emotional bioinformation, most emotion recognition studies relied to ponderous medical grade electroencephalography (EEG) measuring devices. The emotion recognition scenarios were limited to hospitals or laboratories. It is hard to directly apply
these research achievements into the emotion monitoring in daily-life. In this paper, a novel emotion recognition approach based on multimodal wearable biosensor network is investigated. In order to facilitate emotion monitoring in daily-life, a multimodal wearable biosensor network is constructed.
The multimodal bio-signals are acquired by wearable biosensor measuring nodes, and then transmitted to sink node through wireless communication technologies. According to the fuzzy and rough characteristics of human emotions, the fuzzy rough nearest neighbors (FRNN) algorithm is introduced
to classify different emotions. By considering the fuzzy thresholds of EEG concentration, a novel FRNN emotion recognition approach is proposed. The proposed method narrows the classification range of samples with significant concentration and reduces the disturbance of noisy samples, so that
the high accuracy (65.6%) and fast speed were achieved in wearable scenario. Experiments verified the effectiveness of the proposed approach.
Arrhythmia Detection Using Gated Recurrent Unit Network with ECG Signals
Authors: Xu Gang , Xing Guangxin , Jiang Juanjuan , Jiang Jian , Ke Yongsheng
Published: December 2019

Background: Arrhythmia is a kind of heart disorder characterized by irregular heartbeats which can be detected with Electrocardiographic (ECG) signals. Accurate and early detection along with differentiation of arrhythmias is of great importance in a clinical setting. However,
visual analysis of ECG signal is a challenging and timeconsuming work. We have developed an automatic arrhythmia detection model with deep learning framework to expedite the diagnosis of arrhythmia with a high degree of accuracy. Methods: We proposed a novel automatic arrhythmia detection
model utilizing a combination of 1D convolutional neural network (1D-CNN) and Gated Recurrent Unit (GRU) network for the diagnosis of five different arrhythmia on ECG signals taken from the MITBIT arrhythmia physio bank database. Results: The proposed system showed a high classification
performance in handling variable length ECG signal data, achieving an accuracy rate of 99.45%, sensitivity of 98.35% and specificity of 99.21% and a F1-Score of 98.95% using a five-fold cross validation strategy. Conclusions: Combining 1D-CNN and GRU networks yielded a higher
degree of accuracy compared with other deep learning networks. Our proposed arrhythmia detection method may be a powerful tool to aid clinicians in accurately detecting common arrhythmias on routine ECG screening.
The Novel Indices of Short-Time Heart Rate Variability for Prediction of Cardiovascular and Cerebrovascular Events
Authors: Shao Shiliang , Wang Ting , Song Chunhe , Su Yun , Chen Xingchi , Zhao Hai
Published: December 2019

In this paper, eight novel instantaneous indices of short-time heart rate variability (HRV) signals are proposed for prediction of cardiovascular and cerebrovascular events. The indices are based on Bubble Entropy (BE) and Singular Value Decompose (SVD). The process of indices calculation
is as follows, firstly, the instantaneous amplitude (IA), instantaneous frequency (IF) and instantaneous phase (IP) of HRV signals are estimated by the Hilbert transform. Secondly, according to the HRV, IA, IP and IF, the BE and singular value (SV) is calculated, then eight novel indices are
obtained, they are BEHRV, BEIA, BEIF, BEIP, SVHRV, SVIA, SVIF and SVIP. Last but not least, in order to evaluate the performance of the eight novel indices for prediction of cardiovascular and cerebrovascular
events, the difference analysis of eight indices is carried out by t-test. According to the p value, seven of the eight indices BEHRV, BEIA, BEIF, BEIP, SVIA, SVIF and SVIP are thought to be the indices
to discriminate the E group and N group. The K-nearest neighbor (KNN), support vector machine (SVM) and decision tree (DT) are applied on the seven novel indices. The results are that, seven novel indices are significantly different between the events and non-events groups, and the SVM classifier
has the highest classification Acc and Spe for prediction of cardiovascular and cerebrovascular events, they are 88.31% and 90.19%, respectively.
The Neural Response of Deep Brain Structures to Odorant Stimulations: A Manganese-Enhanced MRI Study
Authors: Han Mun , Kim Byungmok , Park Jang Woo , Kim Eunji , Lee Jongmin , Lee Hui Joong , Chang Yongmin
Published: December 2019

An increased understanding of how odors are processed in the central nervous system may provide comprehensive information about the neural basis of odor-related behavior and learning. In this study, we investigated how different odors are processed from the olfactory bulb to the deep
cerebral structures through various olfactory pathways. To do this, we employed a novel manganese-enhanced magnetic resonance imaging (MEMRI) method to map the activity-dependent functional connectivity of the olfactory and non-olfactory pathways associated with various odorants. Our MEMRI
data revealed odor-specific neural pathways that correspond to different odorant stimulations, suggesting that different neural circuits may process different odorants. Among the odorants tested, formic acid, an alarm pheromone, stimulated not only the primary and secondary olfactory pathways
but also the mesolimbic neural circuit, which overlaps with the dopaminergic neural pathway. Linalool, which is a major component of aroma oils, showed high Mn2+ uptake in the hypothalamus, which plays a role in the stress response through the secretion of corticotropin-releasing
hormone (CRH), and consequently, the stimulation of corticotropin secretion. Acetone mainly activated the primary olfactory pathway, whereas saline acted as a non-odorous trigeminal stimulus. Taken together, our functional MEMRI using anatomic standardization and statistical analyses could
be a promising in vivo imaging method to map neural connectivity, enabling further understanding of the neural processing of different odorants.
Abnormal Event Health-Status Monitoring Based on Multi-Dimensional and Multi-Level Association Rules Constraints in Nursing Information System
Authors: Fang Mingjing , Xu Yingying , Yin Qionghua , Yu Jianhong , Wang Can , Zhang Yingying
Published: December 2019

Since the traditional methods such as statistical analysis are used to analyze the long-term nursing service strategy more, and no association rule method is used to solve the problems encountered in the development of long-term nursing service. This paper aims to provide some data
support for the establishment of a scientific and reasonable long-term nursing information system by synthesizing the basic national conditions and drawing lessons from foreign experience. Therefore, in order to improve the analytical accuracy of multidimensional multi-model data, minimal
rule which includes rules with single item as the consequent and the minimal number of items as the antecedent, can be introduced to derive the same decisions as other association rules without information loss, while the number of minimal rules is much less than of all rules. From these association
rules, it can be known that diabetes has a strong association with coronary heart disease, hypertension, fatty liver, chronic arterial occlusive disease, abnormal lipid metabolism and other diseases; according to the analysis of the antecedent and consequence in the results table of the above
association rules, it can be found that diabetes is highly likely to cause hypertension, coronary heart disease, abnormal lipid metabolism, fatty liver, and there is a close relationship between various complications.
Genetic Based Face Recognition for Healthcare Applications
Authors: Deepa S. , Chamundeeswari V. Vijaya
Published: December 2019

Face recognition is a significant biometric credential in the field of security authentication. It additionally assumes a noteworthy job in image processing and it is applicable in various systems like verifying the identity of the person and in security purpose. Recognizing the face
with varying background, poses and illumination are the complexity involved in this face recognition. Many algorithms exist for face recognition, of which, Discrete Wavelet Transform (DWT) with Principal Component Analysis (PCA) works better for recognition of faces. An algorithm using 3 Level-DWT
and modified PCA is proposed for feature extraction. The PCA and reconstruction of images using Inverse PCA, help not only for dimensionality reduction, but also to find the least principal components (PC) of an image from which the significant features of a face image can be extracted. The
significant features thus extracted are used for classifying genetic and non-genetic faces. Using extracted features from 3 level DWT and PCA, Support vector machine (SVM) is utilized to classify the faces genetically. The proposed extracted features does not intend to certain features like
ears, nose and eyes of the face, but corresponds to identify the faces which are genetically similar. Based on the statistical measure analysis, the proposed algorithm 3 Level dwt with modified PCA works well in extracting the features for identifying the faces which are genetically closer.
This face recognition application system can be effectively used to treat a patient in other location with complete security. There is no chance for data stealing, since the concerned doctors and patient only will take part in the system. The identification of genetic faces will turn out to
be an achievement in the field of health care monitoring systems.
A Preliminary Study of Gadobenate Dimeglumine Enhanced Magnetic Resonance Angiography Detection of Prostatic Artery Anatomy
Authors: Shen Yanguang , Zhang Jinlong , Song Peng , Ma Lu , Zhong Yan , Zhang Xiaojing , Wang Haiyi , Wang Yingwei , Zhang Hongtao , Sun Zhonghua , Ye Huiyi
Published: December 2019

Background: To describe the feasibility of anatomy and imaging findings of the prostatic arteries (PAs) on contrast-enhanced MR angiography (ce-MRA). Methods: This prospective study enrolled 7 patients undergoing pelvic 3.0 T ce-MRA and selective pelvic digital subtraction
angiography before prostatic artery embolization (PAE). Four patients underwent fluoroscopically triggered ce-MRA and 3 patients underwent time-resolved imaging of contrast kinetics. PAE was performed within one week of MRA. Two radiologists independently analyzed MRA findings. The analyzed
parameters included the number of independent PAs and their origin, trajectory, and anastomoses with adjacent arteries. Image quality in terms of signal-to-noise ratio (SNR) and contrast-tonoise ratio (CNR) to visualize common iliac artery and its branches including prostatic arteries was
compared between the two groups. Results: A total of 17 PAs were demonstrated in 7 cases. The diagnostic accuracy of MRA in detecting bilateral PAs was 58.82%. Moreover, 71.42% of the patients had only one PA in one side, and 7.14% had independent PAs. The mean diameter of 10 PAs was
1.39 ± 0.46 mm. About 29.41% of PAs originated from the obturator arteries and 29.41% from the pudendal arteries. Bilateral PAs were found to be symmetric in 3 cases and asymmetric in 4 cases. No significance difference was found between the image quality of the two groups, except for
the signal intensity of the right common iliac artery and right internal iliac artery and SNR and CNR of the left common iliac artery. Conclusions: This study shows that ce-MRA is able to detect PA, and can provide guidance for PAE.
Hybrid Approach Towards Removal of Streak Artifacts and Region Identification in Brain Radiology
Authors: Bhagyalakshmi A. , Chamundeeswari V. Vijaya
Published: December 2019

A streak artifact is an anomaly seen in the CT image of the brain. It is like a dark bands or streaks appearing in an image that is not present in the original region. The appearance of these artifacts leads to misinterpretation of medical diagnosis. Under sampling, photon starvation,
motion and beam hardening are some of the reasons for occurrence of streak artifacts. These type of artifacts commonly occur in the posterior fossa of the brain. This paper mainly focuses on region shape and texture identification and removal of streak artifacts exists in the brain image.
Initially morphological operators and fuzzy k-means algorithms are used to identify the different regions in the CT brain image. Each region is treated as a single image and features are extracted for each image. The feature descriptors are constructed using region properties and statistical
features. The extracted features are compared with threshold brain image to identify the dark bands of streak artifacts.
The Role of MRI in the Diagnosis of Papillary Thyroid Carcinoma with Hobnail Features: A Case-Control Study
Authors: Wang Lanyun , Song Bin , Wang Hao , Chen Yongqi , Liu Weiyan , Wei Ran , Dai Zedong , Hu Wenjuan , Ding Yi
Published: December 2019

Purpose—To evaluate whether Magnetic Resonance Imaging (MRI) preoperatively could be used as a potentially routine tool to recognize papillary thyroid carcinoma with hobnail features (HPTC). Methods—This retrospective study included 97 patients with confirmed
papillary thyroid carcinoma (PTC) who underwent thyroid MRI examination one week before surgery between January 2014 and June 2017. The differences in age, sex, tumor size, each MRI feature (lesion shape; signal heterogeneity on T1WI, T2WI and diffusion weighted imaging (DWI); significantly
high signal on T2WI, segmentation linear low signal on T2WI, T2WI lobulation, T2WI ripple-like performance; and early enhancement degree, delayed enhancement pattern) and apparent diffusion coefficient (ADC) values of HPTC and PTC without hobnail features (NHPTC) were determined. The evaluation
indicators of significant MRI features were determined. Independent-samples t test, chi-square test, Fisher's exact test, nonparametric test, and analyses of receiver operating characteristic (ROC) curve were performed. Results—There were 10 patients with HPTCs and 81 patients
with NHPTCs included in the study. Compared to the NHPTCs, the HPTCs had larger volumes (P = 0.025), more irregular shapes (P = 0.004), more heterogeneous T2WI and DWI signals (P = 0.028, 0.006, respectively), higher frequencies of significantly high signals on T2WI, segmentation
linear low signals, lobulation and ripple-like performances (P = 0 000 all), and higher ADC values (P = 0 036). In addition, the delayed mass-like enhancement was more common in HPTCs (P = 0.046). Characteristics including significantly high signal on T2WI, segmentation
linear low signal, lobulation, ripplelike performance and a size ≥2.15 cm have significant power to predict HPTCs. Characteristics including the absence of irregular shape, a heterogeneous signal on T2WI or a significantly high signal on T2WI have significant power to predict NHPTCs. Conclusions—HPTC
lesions have unique MRI features distinct from NHPTCs and recognizing these features may help to assess the invasiveness of PTC preoperatively.
Towards Computational Tool Support for Human Health
Authors: Malik Muhammad Noman , Khan Huma Hayat , Subhan Fazli , Basharat Zeeshan , Khan Hussain Ali
Published: December 2019

The demand for applications of intelligent computing for human life is increasing, as the load of work, tension and hectic routine always challenge individual life. It is really getting difficult to manage an adequate amount of time. Especially for planning balance diet, proper usage
of drugs and medications, sufficient sleep time for an active mind, steadiness in exercise and others that eventually create imbalance to live healthy life. A very little focus is conducted on such important considerations. This, in order to support such important planning and decisions, we
introduce a Sleep Well Live Fit (SWeLF) android based software computational tool that can assist to record our daily routine activities including the intake of meals, drugs and medications, working hours, physical exercise (via sensors) and others. The prime objective of this tool is to inform
and suggest the number of calories consumed on the basis of his routine and about the amount of sleep essentially required for healthy life. In this paper, we examine the core features of the SWeLF tool to support the computing in a human life in comparison of existing methodologies. A detail
experiment methodology is conducted to investigate the practicality, and demonstrated the usability of SWeLF tool in E-Health domain that eventually could help individuals to live fit.
Heart Sound Classification Using Multi Modal Data Representation and Deep Learning
Authors: Lee Jang Hyung , Kyung Sun Young , Chun Oh Pyung , Kim Kwang Gi , Shin Dong Jin
Published: December 2019

Heart anomalies are an important class of medical conditions from personal, public health and social perspectives and hence accurate and timely diagnoses are important. Heartbeat features two well known amplitude peaks termed S1 and S2. Some sound classification models rely on segmented
sound intervals referenced to the locations of detected S1 and S2 peaks, which are often missing due to physiological causes and/or artifacts from sound sampling process. The constituent and combined models we propose are free from segmentation, which consequently is more robust and meritful
from reliability aspects. Intuitive phonocardiogram representation with relatively simple deep learning architecture was found to be effective for classifying normal and abnormal heart sounds. A frequency spectrum based deep learning network also produced competitive classification results.
When the classification models were merged in one via SVM, performance was seen to improve further. The SVM classification model, comprised of two time domain submodels and a frequency domain submodel, produced 0.9175 sensitivity, 0.8886 specificity and 0.9012 accuracy.
Effect of heat treatment on microstructure and corrosion resistance of extruded ZK80 Mg alloy
Authors: Cao Fenghong , Chen Chang , Wang Zhenyu
Published: December 2019

The corrosion characteristics and corrosion mechanism of the extruded ZK80 alloy with different states soaking in 3.5% NaCl solution at room temperature were analyzed via OM, SEM, EDS, XRD and static weightlessness method and other experimental analysis methods. The results show that
when the aging temperature is constant, and the corrosion rate decreases with the lengthen of aging time, while when the corrosion time is constant, the corrosion rate increases with the increase in aging time. Appropriate aging treatment not only refines the grain of the alloy, but also precipitates
the Mg–Zn phase which can effectively prevent the corrosion process and improve the anti-corrosion properties of the alloy. The main corrosion characteristics of the alloy are filamentary corrosion and pitting corrosion.
Effect of steel slag powder, fly ash, and silica fume on the mechanical properties and durability of cement mortar
Authors: Lun Yunxia , Zheng Fangfang
Published: December 2019

This study is aimed at exploring the effect of steel slag powder (SSP), fly ash (FA), and silica fume (SF) on the mechanical properties and durability of cement mortar. SSP, SF, and FA were used as partial replacement of the Ordinary Portland cement (OPC). It was showed that the compressive
and bending strength of steel slag powder were slightly lower than that of OPC. An increase in the SSP content caused a decrease in strength. However, the growth rate of compressive strength of SSP2 (20% replacement by the weight of OPC) at the curing ages of 90 days was about 8% higher than
that of OPC, and the durability of SSP2 was better than that of OPC. The combination of mineral admixtures improved the later strength, water impermeability, and sulfate resistance compared with OPC and SSP2. The compressive strength of SSPFA (SSP and SF) at 90 days reached 70.3 MPa. The results
of X-ray diffraction patterns and scanning electron microscopy indicated that SSP played a synergistic role with FA or SF to improve the performance of cement mortar.
Holey graphene oxide as filler to improve electrochemical performance of solid polymer electrolytes
Authors: Wang Qi , Zhang Zhoujie , Shen Fei , Zhao Bin , Han Xiaogang
Published: December 2019

Solid polymer electrolytes (SPE) have attracted wide attention of researchers because of their high safety performance and high mechanical strength. In this paper, holey graphene oxide (HGO) was added to poly(ethylene oxide) (PEO) solid polymer electrolytes with lithium bis(trifluoromethane
sulfonimide) (LiTFSI) as salt to improve the ionic conductivity of solid polymer electrolytes. It was shown that the addition of holey graphene oxide improved the electrochemical window and ionic conductivity. When the amount of holey graphene oxide was 0 wt%, the ionic conductivity was 2.06
× 10–4 S/cm at 60 °C. In comparison, when the amount of holey graphene oxide was 0.2 wt%, the ionic conductivity was greatly increased to 6.05 × 10–4 S/cm. This was mainly due to the fact that addition of holey graphene oxide reduced the crystallization
of polymer and promoted the migration of lithium ion. Meanwhile, the electrochemical window was expanded to 5.2 V and the cycle performance for the batteries was also improved.
Nano-silver-containing polyvinyl alcohol composite film for grape fresh-keeping
Authors: Deng Jing , Chen Qi Jue , Peng Zi Yu , Wang Jian Hui , Li Wen , Ding Quan Ming , Lin Qin Lu , Liu Dong Min , Wang Sha Sha , Shi Yi , Liu Yu , Feng Shan Shan
Published: December 2019

Nano-composite film possesses antibacterial properties of nanomaterials and preservation performance of food packaging film. The goal of this work was to explore the potential application of nano-silver-containing polyvinyl alcohol (PVA) composite film in grape fresh-keeping. Mechanical
properties, transparency, barrier performance and antibacterial activity of Nano-silver-containing PVA film were tested and results showed that the antibacterial zone diameter of nano-silver on Aspergillus niger was 14.4 mm, with minimum bactericidal concentration of 75 ppm. The PVA
antibacterial membrane effectively revealed antibacterial properties. Moreover, the decreased mechanical properties and transparency enhanced barrier performance of antibacterial membrane compared with PVA film. For comparison, the fresh-keeping effects of non-packaging film, commercial cling
wrap and nano-silver-containing PVA film on sensory score, mass loss rate, decay rate, Ascorbic Acid (Vc) content and titratable acid content were also studied. Results indicated that the PVA antibacterial membrane significantly effectively avoided sensory score, water, decay, Ascorbic Acid
(Vc) and titratable acid in grapes during storage, and had a remarkable effect in prolonging grapes' shelf life due to its breathability and antibacterial activity.
On the microstructure and mechanical properties of silver-bearing antibacterial CD4MCu duplex stainless steels: Solid solution temperature
Authors: Xiang Hongliang , Liu Dong , Chen Xueping , Cao Huatang , Dong Xuanpu
Published: December 2019

The Ag-bearing antibacterial stainless steel has attracted substantial attention in the field of bacterial proliferation prevention. In this study, a Cu–Ag alloy was incorporated into a CD4MCu duplex stainless steel (DSS) to produce a good antibacterial property. The Ag-bearing
CD4MCu duplex stainless steel samples were conducted solid solution treatment at various temperatures. The effects of the solid solution temperature on the microstructure, mechanical and corrosion properties, as well as silver ion release characteristics and antibacterial properties were investigated.
Results show that apart from the original α, γ phases, Ag-bearing phases were formed in all samples after solid solution at different temperatures. The volume fraction of α and Ag-bearing phases decrease with increasing solution temperature. The tensile strength, hardness,
elongation, and corrosion resistance first decrease and then increase along with the increase in the solid solution temperature. The heat treatment conducted at 1150 °C increases the strength, the hardness, the pitting corrosion resistance, and the antimicrobial capability simultaneously.
The antibacterial rates of all the Ag-bearing CD4MCu duplex stainless steel samples after solid solution treatment are above 99%, indicating their good anti-bacterial capabilities.
CIAPIN1 is a potential target for apoptosis of multiple myeloma
Authors: Wang Xiao-Bo , Yan Le-Ping , Yuan Li-Hua , Lu Bo , Lin Dong-Jun , Xu Xiao-Jun
Published: December 2019

This study firstly aimed to reveal the gene expression differences of CIAPIN1 between myelomas cells from bone marrow cells of multiple myeloma patients and normal human, and subsequently investigate the regulation role of this gene on tumorigenicity ability of multiple myeloma (MM)
cell line U266 via in vitro colony formation and in vivo xenograft studies. RT-PCR results obtained from 18 MM patients and 10 health people showed that the expression of CIAPIN1 gene was 4 times higher in normal human compared to MM patients. Besides, CIAPIN1 siRNA (si-CIAPIN1) transfected
U266 cells presented higher proliferation ratio and superior colony forming ability than U266 cells and U266 cells transfected with non-coding siRNA (controls) evaluated by CCK8 test and soft agar colony formation assay, respectively. In a mice MM xenograft model, the si-CIAPIN1 transfected
U266 cells induced the biggest tumor compared to the controls. Furthermore, CIAPIN1 overexpressed U266 cells were developed and compared with the si-CIAPIN1 transfected U266 cells to study the role of CIAPIN1 in the production of apoptosis related proteins in U266 cells. Results indicated
that CIAPIN1 facilitated apoptosis promoting proteins expression in U266 cells, such as upregulation of BAX, BAK, Bcl-xs and BIM, and downregulation of p38, PKC, Bcl-2 and Bcl-xl proteins. Therefore, CIAPIN1 can be a potential suppression target gene in multiple myeloma.
Investigation on the preparation of nanoparticles by inverse micro-emulsion polymerization for enhanced oil recovery
Authors: Zhang Dong , Zhou Runnan , Fang Xinren , Qiu Yue
Published: December 2019

Polyacrylamide nanoparticles attracted increasing interest as profile control agents in oilfield development owing to the characteristics of deformation and continuous expansion in core pore. In this article, the structure and morphology of polyacrylamide nanoparticles were characterized
by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). And the fluorescence microscopy and core displacement equipment were used to evaluate the properties of the expansion and plugging properties. Results showed that the nanoparticles prepared by inverse
emulsion polymerization can be able to pass through the pores in the rocks due to the uniform particle size which is less than 500 nm. When the nanoparticles were in contact with water for 72 hours, the average particle size increased to 12.2 μm. The water-swelled nanoparticles can
effectively block the high-permeability layer and improve the oil recovery in the low-permeability layer. The results obtained in this paper reveal the mechanism of elastic deformation and expansion characteristics of polyacrylamide nanoparticles, and further prove the great potential of nanoparticles
to enhance oil recovery in the development of low permeability oilfields.
Stakeholders’ Perspectives of Reentry to School and Community for Young Offenders With Disabilities: An Ecological Approach
Authors: Unruh Deanne , Waintrup Miriam , Alverson Charlotte , Erickson MaryJo , MaGee Caroline
Published: December 2019

Reentry services for young offenders need to be youth-focused and inclusive of the multiple entities that support a youth’s return to the community. The purpose of this study was to gain a better understanding from stakeholders’ perspectives, based on experience, to identify challenges and facilitators to support young offenders’ reengagement in school and successful return to their community postincarceration. Fifty-four qualitative interviews of key stakeholders involved in the reentry process were conducted across 4 years of a young offender with disabilities’ reentry program in the Northwest. Stakeholders included youth, parents, school personnel, parole/probation officers, and other community agency personnel. An ecological framework coding scheme was used to qualitatively analyze the interviews. Findings validate the importance a key adult/mentor (i.e., transition specialist) plays in a youth’s school engagement. In addition, developing strong working relationships within and across schools, juvenile services, and other key agency personnel was supportive of a young offender’s reentry process.
Managing Fiscal Volatility: An Empirical Analysis of California County Governments’ Saving Behavior
Authors: Shon Jongmin , Kwak Sunjoo
Published: December 2019

The increase in the importance of countercyclical behavior has expanded the research on fiscal saving behavior to local governments. In particular, the Great Recession has shown that local governments are not immune to economic shocks, spurring interest in local savings behavior. County governments are particularly vulnerable to negative economic shocks, as they rely more on intergovernmental revenues. With a focus on the determinants of fiscal slack, we empirically examined the relationship between tax revenue volatility and unassigned fund balance in 57 California counties over the period of 2004 to 2014. Employing spatial regression models, our empirical analysis revealed that revenue volatility is positively associated with general unassigned fund balance in California counties, and revenue diversification has partially positive effects on the fund balance. We infer that tax revenue volatility threatens the stabilized delivery of local services, which suggests that local governments should look to the factors that potentially affect revenue stability to improve their capacity for financial management. The spillover effects from the findings suggest that spatial effects need to be taken into account in analyzing the determinants of local fiscal slack.
Cannabinoids for the Neuropsychiatric Symptoms of Dementia: A Systematic Review and Meta-Analysis
Authors: Bahji Anees , Meyyappan Arthi Chinna , Hawken Emily R.
Published: December 2019

Background:

In 2016, the global number of individuals living with dementia was 43.8 million, representing a 117% increase from 1990—mainly due to increases in aging and population growth. Up to 90% of individuals with dementia experience neuropsychiatric symptoms (NPS). However, the limitations of current treatments for NPS have drivent he search for safer pharmacotherapies—including cannabinoids.

Aim:

To assess the efficacy and acceptability of cannabinoids for the treatment of NPS in individuals with dementia.

Design:

Systematic review and meta-analysis of clinical trials.

Setting and participants:

Of 6,902 papers, 9 were eligible ( n = 205, 44% female, 78 ± 7 years, 85% Alzheimer disease). Trials were in North America and Europe and explored tetrahydrocannabinol ( n = 3), dronabinol ( n = 5), or nabilone ( n = 1).

Measurement:

Titles/abstracts were independently screened by one reviewer and reviewed by a second. Full-text screening was by two reviewers with discrepancies resolved via a third reviewer. We extracted data on the standardized mean difference (SMD) for several NPS instruments, trial completion, and adverse events. Data were pooled using random-effects models.

Findings:

Cannabinoids led to significant improvements across NPS instruments, including the Cohen Mansfield Agitation Inventory (SMD = −0.80; 95% confidence interval [CI], −1.45 to −0.16), the Neuropsychiatric Inventory (SMD = −0.61; CI, −1.07 to −0.15), and nocturnal actigraphy (SMD = −1.05; CI, −1.56 to −0.54h). Cannabinoids were well-tolerated, with an overall trial completion rate of 93% (193/205) and no serious treatment-related adverse events. Treatment efficacy was associated with baseline dementia severity and dose, but not dementia subtype, age, or sex. The overall study quality was rated as low.

Conclusions:

There is preliminary evidence for the efficacy and tolerability of cannabinoids as treatments for NPS. Population-based studies are needed to characterize their real-world effectiveness and acceptability.
Economic Burden of Depression and Associated Resource Use in Manitoba, Canada
Authors: Tanner Julie-Anne , Hensel Jennifer , Davies Paige E. , Brown Lisa C. , Dechairo Bryan M. , Mulsant Benoit H.
Published: December 2019

Objectives

To characterize the health-care utilization and economic burden associated with depression in Manitoba, Canada.

Methods

Patient-level data were retrieved from the Manitoba Centre for Health Policy administrative, clinical, and laboratory databases for the study period of January 1, 1996, through December 31, 2016. Patients were assigned to the depression cohort based on diagnoses recorded in hospitalizations and outpatient physician claims, as well as antidepressant prescription drug claims. A comparison cohort of nondepressed subjects, matched with replacement for age, gender, place of residence (urban vs. rural), and index date, was created. Demographics, comorbidities, intentional self-harm, mortality, health-care utilization, prescription drug utilization, and costs of health-care utilization and social services were compared between depressed patients and matched nondepressed patients, and incidence rate ratios and hazard ratios were reported.

Results

There were 190,065 patients in the depression cohort and 378,177 patients in the nondepression cohort. Comorbidities were 43% more prevalent among depressed patients. Intentional self-harm, all-cause mortality, and suicide mortality were higher among patients with depression than the nondepression cohort. Health-care utilization—including hospitalizations, physician visits, physician-provided psychotherapy, and prescription drugs—was higher in the depression than the nondepression cohort. Mean health-care utilization costs were 3.5 times higher among depressed patients than nondepressed patients ($10,064 and $2,832, respectively). Similarly, mean social services costs were 3 times higher ($1,522 and $510, respectively). Overall, depression adds a total average cost of $8,244 ( SD = $40,542) per person per year.

Conclusions

Depression contributes significantly to health burden and per patient costs in Manitoba, Canada. Extrapolation of the results to the entire Canadian health-care system projects an excess of $12 billion annually in health system spending.
Geometry Interventions for Students With Learning Disabilities: A Research Synthesis
Authors: Liu Meijia , Bryant Diane Pedrotty , Kiru Elly , Nozari Maryam
Published: December 2019

The purpose of this study was to examine geometry interventions for students with learning disabilities. We synthesized nine intervention studies by focusing on the geometry concepts and skills taught to students with learning disabilities, intervention types used, instructional components embedded, and the methodological rigor of the studies. Intervention studies were mainly single-case designs. The geometry topics included angle recognition, and perimeter, area, and volume problems. The findings of this synthesis contribute to the current literature by showing that geometry interventions for students with learning disabilities incorporated proven effective instructional components (e.g., multiple representations, skill modeling). Regarding the methodological rigor, the results showed several issues, including the lack of description of interventionist training, intervention treatment fidelity, and adequate technical information for student outcome measures. Limitations of the studies and suggestions for future intervention research are discussed.
Ankyloglossia Identification, Diagnosis, and Frenotomy
Authors: Unger Crystal , Chetwynd Ellen , Costello Rebecca
Published: December 2019

Background

Researchers and practitioners continue to debate the most appropriate assessment, diagnostic, and treatment practices for ankyloglossia (tongue-tie). Health care workers struggle to provide evidence-based care in the absence of consistent standards.

Research aim

The aims of this pilot study were to qualitatively (a) evaluate the knowledge of, and attitudes toward tongue-tie and (b) describe how they shaped referral pathways and the establishment of practice patterns of frontline practitioners (pediatric dentists, speech-language pathologists, pediatric chiropractors, and International Board Certified Lactation Consultants).

Methods

We recruited clinicians ( N = 9) using nonprobability purposive sampling. Participants were interviewed using survey schedules adjusted to reflect their specialty area. Semistructured interviews were transcribed and coded using manual and inductive coding techniques common in grounded theory. Themes were iteratively developed using memoing techniques, in which observations and potential concepts were recorded using the aforementioned codes.

Results

Participants were familiar with a variety of protocols and assessment tools, but did not consistently use them. No formal training about the management of tongue-tie was received through their degree programs. Instead they pursued self-guided study. Interprofessional consensus guided opinions about tongue-tie best practices, and referral pathways reflected these consensuses. International Board Certified Lactation Consultants were viewed as pivotal to the care of infants with tongue-tie while primary care physicians—primarily pediatricians—were omitted from referral pathways.

Conclusion

Lack of formalized training, professional consensus about best practices, and insufficient resources for assessing and treating tongue-tie led participants to incomplete referral pathways and personal interpretations of the data through the lens of anecdotal evidence.