bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Analysis of Supervised Learning Models for Product Title Classification
Authors: Noor Mathivanan Norsyela Muhammad , Md.Ghani Nor Azura , Mohd Janor Roziah
Published: March 2020

Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21st century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is apart of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.
An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification
Authors: Milleana Shaharudin Shazlyn , Ismail Shuhaida , Nor Siti Mariana Che Mat , Ahmad Norhaiza
Published: March 2020

In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and bi-clustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCA-kMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.
A Multiple Mitosis Genetic Algorithm
Authors: Kamil K. , Chong K.H , Hashim H. , Shaaya S.A.
Published: March 2020

Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.
Polarity Classification Tool for Sentiment Analysis in Malay Language
Authors: Abu Bakar Normi Sham Awang , Aziehan Rahmat Ros , Faruq Othman Umar
Published: March 2020

The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentiment lexicon and found 90% score for precision, recall and accuracy. Our main contribution in the research is the new AFINN translation for Malay language and also the classification of the sentiment data.
Review of anomalous sound event detection approaches
Authors: Affendi Amirul Sadikin Md , Yusoff Marina
Published: March 2020

This paper presents a review of anomalous sound event detection(SED) approaches.  SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy.  SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method.  The review compares multiple approaches that is applied on a specific dataset.   Security relies on anomalous events in order to prevent it one must find these anomalous events.  Audio surveillance has become more efficient as that artificial intelligence has stepped up the game.  Autonomous SED could be used for early detection and prevention.  It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network(CRNN) with long short term memory(LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.
Killer Whale – Backpropagation (KW-BP) Algorithm for Accuracy Improvement of Neural Network Forecasting Models on Energy-Efficient Data
Authors: Kamaruddin Saadi Bin Ahmad , Azura Md Ghani Nor , Ab Rahim Hazrita , Musirin Ismail
Published: March 2020

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-for-money(VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved(MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System(BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved(MW/hr) was Building Energy Index(BEI)(kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer Whale - Backpropagation Algorithm(KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come.
Intelligent Risk Management framework
Authors: ABBASS Wissam , BAKRAOUY Zineb , BAINA Amine , BELLAFKIH Mostafa
Published: March 2020

The Internet of Things(IoT) is rapidly increasing and enhancing today’s world by introducing a large set of interconnected devices. Several beneficial services are produced by these devices as for area monitoring and process control. However, IoT security is still a major problem. In fact, IoT’ security beggings largely whith an effective Risk Management process. However, the essense of this process is to acquire a risk inventory cibling the IoT devices. Nevertheless, it is quite difficult to obtaining this latter which significantly adds complication issues to the Risk Management.Without the ability of holisticly identify the IoT critical devices, inaccurate Risk Management is achieved which leads unfortunately to novel risk exposures. Traditional Risk-based approaches fails drastically at apprending IoT’ potential attacks. The dynamic structure, the heteregouns nature of devices, the various security objectives and infrastructure pervasiveness are key factors impacting the overall perfomance. Thus, a holistic Risk Management witihin the IoT is indispensable. Accordingly, we propose an intelligent Risk Management framework using Mobile Agents in order to deliver preventive and responsive assessment.
Hourly wind speed forecasting based on Support Vector Machine and Artificial Neural Networks
Authors: Barhmi Soukaina , Fatni Omkalthoume El
Published: March 2020

Wind speed is the main component of wind power. Therefore, wind speed forecasting is of big importance due to its uses. It permits to plan the dispatch, determine the hours of storage needed, the amount of energy stored that should be used and avoid the big fluctuations in the electrical grid caused by the nature of the renewable energy resources. In this paper, we propose four hybrid models based on Support Vector Machine(SVM) and Artificial Neural Networks(ANNs) or just Neural Networks (NN) for wind speed forecasting. Using the Ordinary Least Squares(OLS) analysis for selecting the parameters more influencing wind speed. Then, a Support Vector Machine and Artificial Neural Networks models are tuned by Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). The performance of these models is evaluated using three statistical indicators: the Mean Square Error(MSE), Mean Error(ME) and Mean Absolute Error(MAE). The results show a better performance of the neural model compared to the support vector machine.
Adaptive ANN based differential protective relay for reliable power transformer protection operation during energisation
Authors: Ahmad Azniza , Lufti Othman Mohammad , Khudsiya Bibi Zainab Kurreemun , Hizam Hashim
Published: March 2020

Power transformer is the most expensive equipment in electrical power system that needs continuous monitoring and fast protection response. Differential relay is usually used in power transformer protection scheme. This protection compares the difference of currents between transformer primary and secondary sides, with which a tripping signal to the circuit breaker is asserted. However, when power transformers are energized, the magnetizing inrush current is present and due to its high magnitude, the relay mal-operates. To prevent mal-operation, methods revolving around the fact that the relay should be able to discriminate between the magnetizing inrush current and the fault current must be studied. This paper presents an Artificial Neural Network(ANN) based differential relay that is designed to enable the differential relay to correct its mal-operation during energization by training the ANN and testing it with harmonic current as the restraining element. The MATLAB software is used to implement and evaluate the proposed differential relay. It is shown that the ANN based differential relay is indeed an adaptive relay when it is appropriately trained using the Network Fitting Tool. The improved differential relay models also include a reset part which enables automatic reset of the relays. Using the techniques of 2nd harmonic restraint and ANN to design a differential relay thus illustrates that the latter can successfully differentiate between magnetizing inrush and internal fault currents. With the new adaptive ANN-based differential relay, there is no mal-operation of the relay during energization. The ANN based differential relay shows better performance in terms of its ability to differentiate fault against energization current. Amazingly, the response time, when there is an internal fault, is 1 ms compared to 4.5 ms of the conventional 2nd harmonic restraint based relay.
Forecasting Financial Budget Time Series: ARIMA Random Walk vs LSTM Neural Network
Authors: Rhanoui Maryem , Yousfi Siham , Mikram Mounia , Merizak Hajar
Published: March 2020

Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis shows that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.
Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification
Authors: Jadhav Sachin B.
Published: March 2020

Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy
Intelligent swarm-based optimization technique for oscillatory stability assessment in power system
Authors: Kamari N. A. M. , Musirin I. , Ibrahim A. A. , Halim S. A.
Published: March 2020

This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization(PSO) technique. Indicators namely synchronizing(Ks)and damping(Kd) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming(EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.
Intelligent Optimization And Management System For Renewable Energy Systems Using Multi-Agent
Authors: Ameur Chahinaze , Faquir Sanaa , Yahyaouy Ali
Published: March 2020

Hybrid energy systems(HES) using renewable energy sources are an interesting solution for power stand-alone systems. However, the energy management of such systems is very complex. This paper presents a Multi Agent System(MAS) framework applied to manage the flow of energy in a hybrid stand-alone system. The proposed system consists of photovoltaic panels and a wind turbine along with batteries as storage units. The proposed MAS architecture composed of different agents(photovoltaic agent, wind turbine agent, supervisor agent, load controller agent, and storage agent) was developed to manage the flow of energy between the energy resources and the storage units for an isolated house. The agent-approach for HES is explained and the proposed MAS is presented and a simulation model is developed in the java agent development environment(JADE). The system was tested with empty batteries and full batteries and results showed that the system could satisfy the load demand while maintaining the level of the batteries between 30%(minimum discharging rate) and 80%(maximum charging rate).
Predictive analytics of university student intake using supervised methods
Authors: Iqbal Basheer Muhammad Yunus , Mutalib Sofianita , Hamimah Abdul Hamid Nurzeatul , Abdul-Rahman Shuzlina , Ab Malik Ariff Md
Published: March 2020

Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia(SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.
Artificial Intelligence in Education: Integrating Serious Gaming into the Language Class ClassDojo Technology for Classroom Behavioral Management
Authors: Benhadj Yassine , Messaoudi Mohammed El , Nfissi Abdelhamid
Published: March 2020

The aim of the study at hand was to examine students' perceptions of game elements used in gamification application. ClassDojo, as a study case, was implemented class-wide in a Moroccan High School EFL classroom. Data was gathered and saved directly through the application. It is qualitative research that opted for structured interviews to collect data. The findings were evaluated in so far as class motivation, participation, cooperation, discipline, attendance, and classroom discourse are concerned. This study has shown a crystal clear improvement in terms of discipline, motivations and classroom participation, suggesting the great need to conduct more research with a view to determine if these areas could be positively or negatively impacted when integrating ClassDojo in classroom management on a large scale. The findings of this study are of much significance to decision makers, curriculum developers, syllabus designers, and teachers in both senior and junior schools in Morocco.
Machine Learning: The New Language for Applications
Authors: Padala Venkatsai Siddesh , Gandhi Kathan , Dasari Pushpalatha
Published: March 2020

Machine learning and artiﬁcial intelligence are becoming a major influence in various research and commercial ﬁelds. This review attempts to explain machine learning techniques and applications in various fields. Challenges and future directions are also proposed, including data analysis suggestions, effective algorithms based on the situation, industrial implementation, organization’s risk tolerance, cost-beneﬁt comparisons and the future of machine learning techniques. Applications discussed in this paper range from technological development and health care to financial issues and sports analytics.
Hybridisation of RF(Xgb) to improve the tree-based algorithms in learning style prediction
Authors: Shamsudin Haziqah , Sabudin Maziani , Yusof Umi Kalsom
Published: March 2020

This paper presents hybridization of Random Forest (RF) and Extreme Gradient Boosting (Xgb), named RF(Xgb) to improve the tree-based algorithms in learning style prediction. Learning style of specific users in an online learning system is determined based on their interaction and behavior towards the system. The most common online learning theory used in determining the learning style is the Felder-Silverman’s Learning Style Model (FSLSM). Many researchers have proposed machine learning algorithms to establish learning style by using the log file attributes. This helps in determining the learning style automatically. However, current researches still perform poorly, where the range of accuracy is between 58%-89%. Hence, RF(Xgb) is proposed to help in improving the learning style prediction. This hybrid algorithm was further enhanced by optimizing its parameters. From the experiments, RF(Xgb) was proven to be more effective, with accuracy of 96% compared to J48 and LSID-ANN algorithm from previous literature.
Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images
Authors: Hijazi Mohd Hanafi Ahmad , Kieu Tao Hwa Stefanus , Bade Abdullah , Yaakob Razali , Saffree Jeffree Mohammad
Published: March 2020

Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.
“Cuícas e pandeiros para Stokowski ouvir!”
Authors: Belchior Pedro
Published: March 2020

Em 1940, no contexto da chamada Política da Boa Vizinhança, o regente Leopold Stokowski (1882-1977) visitou alguns países da América do Sul em turnê com a All-American Youth Orchestra. Por meio da análise de fontes da imprensa e do acervo documental do Museu Villa-Lobos, o presente artigo pretende iluminar aspectos relacionados à gravação do disco Native Brazilian Music, que reuniu, sob a iniciativa de Stokowski, alguns expoentes da música popular do Rio de Janeiro – como Donga, Pixinguinha e João da Baiana. O trabalho discute o papel desempenhado por músicos na esfera diplomática e argumenta que um dos efeitos da Política da Boa Vizinhança foi criar um jogo especular no qual, apesar da pretensa unidade cultural do continente, a aproximação promoveu discussões sobre identidade nacional, diferença e resistência.
Brasil Ilustrado (1877 2 1878) e Félix Ferreira
Authors: Luca Tania Regina de
Published: March 2020

O artigo tem por objeto o periódico Brasil Ilustrado: arquivo de conhecimentos úteis, lançado no Rio de Janeiro em janeiro de 1887. Além de localizar a folha no rol das muitas publicações ilustradas que surgiram ao longo do século XIX, trata-se de precisar a sua natureza, o projeto editorial, as razões que motivaram seus idealizadores e analisar o conteúdo difundido, em termos textuais e imagéticos. Especial destaque merece Félix Ferreira, que efetivamente respondeu pela direção do periódico. No mesmo momento, os leitores tinham a oportunidade de adquirir outro mensário, A Ilustração (1884-1892), impresso em Paris e que regularmente chegava a Lisboa e ao Rio de Janeiro a partir do porto de Bordeaux. A confrontação dos dois impressos convida a refletir acerca das condições de produção de estampas na capital do Império, num momento em que se afigurava urgente obter métodos baratos, rápidos e eficientes de reprodução da informação visual, cuja demanda cresceu de maneira muito significativa ao longo do Oitocentos.
The Dating of the Acta Carpi
Authors: Kozlowski Jan M.
Published: March 2020

In the scholarly discussion on the dating of the Acta Carpi, two main proposals were hitherto formed: either II or III c. CE. The article argues that the earlier dating is probable. The Acta Carpi show many parallels with the Martyrium Polycarpi which can be best explained by a direct relationship between the two texts. Since the Acta Carpi betray more archaic features than the Martyrium Polycarpi, which in light of recent studies should be dated to the end of the second century, it is inferred that the Acta Carpi were written in the II c. CE, most probably in the years 166–168.
Augustine’s Use of Principium in De trinitate 1-7
Authors: Reese Ephrem
Published: March 2020

In his trinitarian theology, Augustine’s use of the term principium undergoes a change which has implications for his political theology. In De trinitate 1-7, the application of this concept to the Father first reflects an earlier usage, which follows a Neoplatonic idea of the divine ἀρχή of the One. However, reflection on scripture and religious polemic force a development, and he gradually abandons the term. While he does not abandon the theological idea of the Father’s special principium, the relations among the divine persons demand a consideration of the principium of the Son in common with the Father, an idea familiar to the debates on filioque. This Augustinian development is like what Erik Peterson identifies in the Cappadocians: a trinitarian theological development which threatens the monarchical political theology that would otherwise appeal to Christian Neoplatonist thinking.
Word and Table: The Origins of a Liturgical Sequence
Authors: Cosgrove Charles H.
Published: March 2020

This article shows that the word-table liturgical sequence set forth in Justin Martyr’s First Apology and attested by at least two other ancient descriptions of Christian community meals is not a departure from Greco-Roman banquet custom but has parallels in the wider culture. The evidence is examined in detail, and a possible rationale for a word-table sequence in a banquet setting is proposed.
Walking barefoot vs. with minimalist footwear – influence on gait in younger and older adults
Authors: Petersen Evi , Zech Astrid , Hamacher Daniel
Published: March 2020

Background

In recent years, minimalist footwear has been increasingly promoted for its use in sportive and recreational activities. These shoes are considered to function naturally like barefoot walking while providing a protective surface. Despite a growing popularity of these shoes in the older population, little is known about the influence of minimalist footwear on gait patterns. This study investigated whether overground walking with minimalist shoes is comparable to barefoot walking regarding gait stability and variability parameters.

Methods

In a randomized within-subject study design, 31 healthy younger (29 ± 4 years) and 33 healthy community-dwelling older adults (71 ± 4 years) volunteered. Participants walked on flat ground, once barefoot and once with minimalist shoes. Gait variability of minimum toe clearance (MTC), stride length, stride time, and local dynamic gait stability were analysed.

Results

The results for both age groups showed significant condition effects (minimalist shoes vs. barefoot walking) for the outcomes of local dynamic stability (p = .013), MTC variability (p = .018), and stride length variability (p < .001) indicating increased local dynamic stability and decreased gait variability during the minimalist shoe condition. Group effects (young vs. older adults) were detected in all gait outcomes.

Conclusion

Walking with minimalist shoes appeared to be associated with better gait performance than walking barefoot in both age groups. Thus, walking with minimalist shoes is not similar to barefoot walking. With respect to reducing the risk of falling, we suggest that minimalist shoes could be an alternative to barefoot walking or a transition option between shoes to barefoot for older adults.
Relationship of lymphovascular invasion with lymph node metastasis and prognosis in superficial esophageal carcinoma: systematic review and meta-analysis
Authors: Yang Jinxin , Lu Zhouyi , Li Lintao , Li Yong , Tan Yulong , Zhang Dekang , Wang An
Published: March 2020

Background

The development of tumor cells inside the lymphatics or blood vessels is known as lymphovascular invasion (LVI). The correlation between LVI, lymph node metastasis (LNM), and the diagnosis of superficial esophageal carcinoma (SEC) remains unclear.

Methods

We searched Embase, PubMed, Web of Science, and Cochrane Library databases for prospective articles to better understand the relationship between LVI, LNM, and SEC diagnosis.

Results

We included 23 articles containing data for 4749 patients (range: 54–598) in our meta-analysis. The hazard ratio between LVI and overall survival (OS) was 1.85 with 95% confidence interval (CI) (1.10–3.11, P = 0.02). LNM rate was higher in SEC patients with LVI than SEC patients without LVI (univariate: OR = 4.94, 95% CI: 3.74–6.53, P < 0.0001; multivariate: OR = 5.72, 95%CI: 4.38–7.4, P < 0.0001). No obvious publication was found.

Conclusions

The results indicate that LVI plays a dominant role in the prognosis of LNM in SEC and in the prognostic prediction for SEC.
Risk factor analysis of insufficient fluid intake among urban adults in Wuxi, China: a classification and regression tree analysis
Authors: Zheng Hao , Fei Juan , Zhang Lan , Zhou Weijie , Ding Zhen , Hu Wenbiao
Published: March 2020

Background

Dehydration due to insufficient fluid intake (IFI) is detrimental to health. This cross-sectional study aimed to assess the fluid intake of urban adults in Wuxi, China, and to identify potential risk factors contributing to IFI.

Methods

Adults were selected from the urban area of Wuxi, China, using a multiple-stage random sampling method. The fluid intake information was obtained with a 24-h self-reported diary over seven consecutive days in both summer and winter of 2015. A classification and regression tree (CART) analysis was conducted to detect the potential risk factors associated with IFI. CART is a machine-learning algorithm that portions the data into subsets by threshold.

Results

A total of 584 adults aged 18–87 years were included. The results showed that the median (P25–P75) values of daily fluid intake of the participants were 1100 (800–1550) mL in summer and 1000 (750–1300) mL in winter. Women had a higher prevalence of IFI than men in both summer (odds ratio (OR) = 2.683, 95% confidence interval (CI): 1.830–3.934) and winter (OR = 2.636, 95% CI: 1.677–4.142). The results of CART analysis showed that, in summer, BMI < 25 kg/m2 (probability: 64.2%) and age < 64 years (probability: 67.4%) were main risk factors of IFI for men, and BMI < 29 kg/m2 (probability: 81.6%) and living in C Community (probability: 86.7%) were main risk factors for women. In winter, age < 40 years (probability: 81.8%) and BMI < 20 kg/m2 (probability: 94.5%) were identified as main risk factors of IFI for men and women, respectively.

Conclusions

Most of the participants living in the study site had IFI. The fluid consumption varied by gender, age, location, and BMI. The findings could be useful for the implementation and optimization of intervention programs by identifying the individuals who may at greater risk of dehydration.
Associations of leptin and adiponectin with incident type 2 diabetes and interactions among African Americans: the Jackson heart study
Authors: Bidulescu Aurelian , Dinh Paul C. , Sarwary Shabir , Forsyth Emily , Luetke Maya C. , King David B. , Liu Jiankang , Davis Sharon K. , Correa Adolfo
Published: March 2020

Background

Growing evidence suggests that leptin is critical for glycemic control. Impaired leptin signaling may also contribute to low adiponectin expression in obese individuals. We assessed the association of leptin and adiponectin with incident type 2 diabetes (T2D), their interactions with sex and obesity status, and mediation by insulin resistance.

Methods

We included study participants from the Jackson Heart Study, a prospective cohort of adult African Americans in Jackson, Mississippi, that were free of T2D at the baseline Exam 1. Incident T2D was defined as new cases at Exam 2 or Exam 3. We created separate Cox regression models (hazard ratios per log-transformed ng/mL of leptin and adiponectin) with and without insulin resistance, HOMA-IR. Mediation by insulin resistance was analyzed. Several interactions were assessed, including by sex, HbA1c, and obesity.

Results

Among our 3363 participants (mean age 53 years, 63% women), 584 developed incident T2D. Leptin was directly associated with incident T2D when modeled without HOMA-IR (HR = 1.29, 95% CI = 1.05–1.58). This direct association between leptin and T2D was significant among men (HR = 1.33, 95% CI = 1.05–1.69), but nonsignificant among women (HR = 1.24, 95% CI = 0.94–1.64); statistical interaction with sex was nonsignificant (p = 0.65). The associations in all participants and in men were nullified by HOMA-IR (HR = 0.99, 95% CI = 0.80–1.22; HR = 1.00, 95% CI = 0.78–1.28, respectively), indicating mediation through insulin resistance (proportion mediated: 1.04), and were not observed in abdominally obese participants. Adiponectin was inversely associated with T2D even after adjustment for HOMA-IR in women (HR = 0.68, 95% CI = 0.55–0.84), but not in men (HR = 0.80, 95% CI = 0.62–1.04). The inverse association was present only among abdominally obese participants, and persisted after adjustment for HOMA-IR.

Conclusions

Among African Americans in the Jackson Heart Study the association of leptin with incident type 2 diabetes was mediated by insulin resistance. This association was present only among abdominally non-obese participants. Differences by sex appeared: men showed a significant association mediated by insulin resistance. Among abdominally obese participants, adiponectin was inversely associated with incident T2D even after adjustment for HOMA-IR. Our results should inform future clinical trials that aim to reduce the burden of type 2 diabetes through the modification of serum levels of leptin and adiponectin.
Association between ethical leadership, ethical climate and organizational citizenship behavior from nurses’ perspective: a descriptive correlational study
Authors: Aloustani Soudabeh , Atashzadeh-Shoorideh Foroozan , Zagheri-Tafreshi Mansoureh , Nasiri Maliheh , Barkhordari-Sharifabad Maasoumeh , Skerrett Victoria
Published: March 2020

Background

Ethical leadership plays an important role in improving the organizational climate and may be have an effect on citizenship behavior. Despite the growing emphasis on ethics in organizations, little attention to has been given this issue. The purpose of this study was to identify ethical leadership, an ethical climate, and their relationship with organizational citizenship behavior from nurses’ perspective.

Methods

In this descriptive correlational study, 250 nurses in twelve teaching hospitals in Tehran were selected by multistage sampling during 2016–2017. The data were collected using Ethical Leadership Questionnaire, Hospital Ethical Climate Survey, and Organizational Citizenship Behavior Scale.

Results

The findings showed a significant correlation between ethical leadership in managers, organizational citizenship behavior (P = 0.04, r = 0.09) and an ethical climate (P < 0.001, r = 0.65). There was a significant correlation between an ethical climate and nurses’ organizational citizenship behavior (P < 0.001, r = 0.61). The regression analysis showed that ethical leadership and an ethical climate is a predictor of organizational citizenship behavior and confirms the relationship between the variables.

Conclusion

Applying an ethical leadership style and creating the necessary conditions for a proper ethical climate in hospitals lead to increased organizational citizenship behavior by staff. To achieve organizational goals, nurse managers can use these concepts to enhance nurses’ satisfaction and improve their performance.
Bivalent promoter hypermethylation in cancer is linked to the H327me3/H3K4me3 ratio in embryonic stem cells
Authors: Dunican Donnchadh S. , Mjoseng Heidi K. , Duthie Leanne , Flyamer Ilya M. , Bickmore Wendy A. , Meehan Richard R.
Published: March 2020

Background

Thousands of mammalian promoters are defined by co-enrichment of the histone tail modifications H3K27me3 (repressive) and H3K4me3 (activating) and are thus termed bivalent. It was previously observed that bivalent genes in human ES cells (hESC) are frequent targets for hypermethylation in human cancers, and depletion of DNA methylation in mouse embryonic stem cells has a marked impact on H3K27me3 distribution at bivalent promoters. However, only a fraction of bivalent genes in stem cells are targets of hypermethylation in cancer, and it is currently unclear whether all bivalent promoters are equally sensitive to DNA hypomethylation and whether H3K4me3 levels play a role in the interplay between DNA methylation and H3K27me3.

Results

We report the sub-classification of bivalent promoters into two groups—promoters with a high H3K27me3:H3K4me3 (hiBiv) ratio or promoters with a low H3K27me3:H3K4me3 ratio (loBiv). HiBiv are enriched in canonical Polycomb components, show a higher degree of local intrachromosomal contacts and are highly sensitive to DNA hypomethylation in terms of H3K27me3 depletion from broad Polycomb domains. In contrast, loBiv promoters are enriched in non-canonical Polycomb components, show lower intrachromosomal contacts and are less sensitive to DNA hypomethylation at the same genomic resolution. Multiple systems reveal that hiBiv promoters are more depleted of Polycomb complexes than loBiv promoters following a reduction in DNA methylation, and we demonstrate that H3K27me3 re-accumulates at promoters when DNA methylation is restored. In human cancer, we show that hiBiv promoters lose H3K27me3 and are more susceptible to DNA hypermethylation than loBiv promoters.

Conclusion

We conclude that bivalency as a general term to describe mammalian promoters is an over-simplification and our sub-classification has revealed novel insights into the interplay between the largely antagonistic presence of DNA methylation and Polycomb systems at bivalent promoters. This approach redefines molecular pathologies underlying disease in which global DNA methylation is aberrant or where Polycomb mutations are present.
Field trial to evaluate the effect of an intranasal respiratory vaccine protocol on bovine respiratory disease incidence and growth in a commercial calf rearing unit
Authors: Sandelin Atte , Härtel Heidi , Seppä-Lassila Leena , Kaartinen Liisa , Rautala Helena , Soveri Timo , Simojoki Heli
Published: March 2020

Background

Bovine respiratory disease (BRD) continues to be great challenge in calf rearing units. The urgent need to decrease the use of antibiotics and increase animal welfare in beef production has forced us to introduce new preventive methods. Vaccinations could contribute to the solution, but the high incidence of BRD already at an early age has made it difficult to introduce suitable vaccination programs. Challenge studies have shown promising results in 3–14 day old calves vaccinated with intranasal BRD vaccine, but very few field trials are available to assess the efficacy of the intranasal vaccines in field conditions. We evaluated the effect of one dose of commercial intranasal vaccination on calf mortality, daily gain, and treatment incidence for BRD in one calf rearing unit. In total, 497 calves (mean age 19 days) were included in our study, 247 of which were vaccinated at the time of arrival to the unit and 250 served as negative controls (unvaccinated). Vaccinated and unvaccinated calves were situated in separate compartments until weaning. Daily gain, treatment incidence, and mortality were recorded until the calves were transported to the finishing unit, which averaged 154.5 days from arrival.

Results

Average daily gain over the complete study period was 1151.9 g/day (SD 137.9) for the vaccinated calves and 1139.5 g/day (SD 135.9) for the unvaccinated calves. Intranasal vaccination combined with older arrival age (17 days or older) resulted in a higher daily gain (47.8 g/day) compared with unvaccinated calves (coef. 0.0478, p = 0.003). This association was not recorded in calves that were younger than 17 days upon arrival. Intranasal vaccination was not significantly associated either with mortality (OR 0.976, p = 0.968) or treatment incidence for BRD (OR 1.341, p = 0.120). In total, six vaccinated calves (2.43%) and six unvaccinated calves (2.40%) died during the study period.

Conclusions

Vaccinating arriving calves with intranasal vaccine in the calf rearing unit did not decrease the mortality or treatment incidence for BRD, but it significantly improved the weight gain in calves transported to the unit at the age of 17 days or older.
The equine gastrointestinal microbiome: impacts of weight-loss
Authors: Morrison Philippa K. , Newbold Charles J. , Jones Eleanor , Worgan Hilary J. , Grove-White Dai H. , Dugdale Alexandra H. , Barfoot Clare , Harris Patricia A. , Argo Caroline McGregor
Published: March 2020

Background

Obesity is an important equine welfare issue. Whilst dietary restriction is the most effective weight-loss tool, individual animals range in their weight-loss propensity. Gastrointestinal-derived bacteria play a fundamental role in host-health and have been associated with obesity and weight-loss in other species. This study evaluated the faecal microbiome (next-generation sequencing of 16S rRNA genes) of 15 obese Welsh Mountain pony mares, in the same 11-week period across 2 years (n = 8 Year 1;n = 7 Year 2). Following a 4-week acclimation period (pre-diet phase) during which time individuals were fed the same hay to maintenance (2% body mass (BM) as daily dry matter (DM) intake), animals underwent a 7-week period of dietary restriction (1% BM hay as daily DM intake). Faeces were sampled on the final 3 days of the pre-diet phase and the final 3 days of the dietary restriction phase. Bacterial communities were determined using Next Generation Sequencing of amplified V1-V2 hypervariable regions of bacterial 16S rRNA.

Results

Losses in body mass ranged from 7.11 to 11.59%. Changes in the faecal microbiome composition following weight-loss included a reduction in the relative abundance ofFirmicutesandTenericutesand a reduction in indices of bacterial diversity. Pre-diet diversity was negatively associated with weight-loss. Pre-diet faecal acetate concentration was a strong predictor of subsequent weight-loss and negatively associated withSphaerochaeta (Spirochaetesphylum) abundance. When animals were divided into 3 groups (high, mid, low) based overall weight loss, pre-diet bacterial community structure was found to have the greatest divergence between the high and low weight-loss groups (R = 0.67,p <  0.01), following PERMANOVA and ANOSIM analysis.

Conclusions

Weight-loss in this group of ponies was associated with lower pre-diet faecal bacterial diversity and greater pre-diet acetate concentration. Overall, these data support a role for the faecal microbiome in weight-loss propensity in ponies and provide a baseline for research evaluating elements of the faecal microbiome in predicting weight-loss success in larger cohorts.
Novel plasmid-free Gluconobacter oxydans strains for production of the natural sweetener 5-ketofructose
Authors: Battling Svenja , Wohlers Karen , Igwe Chika , Kranz Angela , Pesch Matthias , Wirtz Astrid , Baumgart Meike , Büchs Jochen , Bott Michael
Published: March 2020

Background

5-Ketofructose (5-KF) has recently been identified as a promising non-nutritive natural sweetener. Gluconobacter oxydans strains have been developed that allow efficient production of 5-KF from fructose by plasmid-based expression of the fructose dehydrogenase genes fdhSCL of Gluconobacter japonicus. As plasmid-free strains are preferred for industrial production of food additives, we aimed at the construction of efficient 5-KF production strains with the fdhSCL genes chromosomally integrated.

Results

For plasmid-free 5-KF production, we selected four sites in the genome of G. oxydans IK003.1 and inserted the fdhSCL genes under control of the strong P264 promoter into each of these sites. All four recombinant strains expressed fdhSCL and oxidized fructose to 5-KF, but site-specific differences were observed suggesting that the genomic vicinity influenced gene expression. For further improvement, a second copy of the fdhSCL genes under control of P264 was inserted into the second-best insertion site to obtain strain IK003.1::fdhSCL2. The 5-KF production rate and the 5-KF yield obtained with this double-integration strain were considerably higher than for the single integration strains and approached the values of IK003.1 with plasmid-based fdhSCL expression.

Conclusion

We identified four sites in the genome of G. oxydans suitable for expression of heterologous genes and constructed a strain with two genomic copies of the fdhSCL genes enabling efficient plasmid-free 5-KF production. This strain will serve as basis for further metabolic engineering strategies aiming at the use of alternative carbon sources for 5-KF production and for bioprocess optimization.