A Review of Influenza Detection and Prediction Through Social Networking Sites

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.https://doi.org/10.1186/s12976-017-0074-

Protein Engineering of a Dye Decolorizing Peroxidase from Pleurotus ostreatus For Efficient Lignocellulose Degradation

Dye decolorizing peroxidases (DyPs) have received extensive attention due to their biotechnological importance and potential use in the biological treatment of lignocellulosic biomass. DyPs are haem-containing peroxidases which utilize hydrogen peroxide (H2O2) to catalyse the oxidation of a wide range of substrates. Similar to naturally occurring peroxidases, DyPs are not optimized for industrial utilization owing to their inactivation induced by excess amounts of H2O2. Furthermore, DyPs are active only under acidic conditions and typically lose activity at neutral or alkaline pH. A dye decolorizing peroxidase from the Pleurotus ostreatus (Pleos-DyP4) was identified recently as a first fungal DyP oxidizing Mn2+ to Mn3+ similar to other fungal peroxidases. However, despite its unique pH and thermal stability, similar to other DyPs, it is not suited for industrial applications. Protein engineering methods are widely used to enhance the stability and catalytic efficiency of biocatalysts to render them suitable for industrial purposes. Different directed evolution approaches (namely, error-prone PCR and saturation mutagenesis) were used to construct mutant libraries of DyP4. For protein expression studies, the mutant enzymes were co-expressed with OsmY protein (a novel secretion-enhancing protein) in order to secrete intracellular protein into the media and hence facilitate the screening of mutants. ABTS assay was used to screen for mutants with improved activities in 96-well microtiter plates. Four rounds of error-prone PCR (epPCR) and saturation mutagenesis led to the identification of a mutant with an approximately 10-fold improvement in total activity and resistance to H2O2 inactivation in comparison with the wild type (WT). This study showcases the usefulness of the OsmY-based secretion mechanism of protein in E. coli as a tool in facilitating the screening of DyP4 mutants, and potentially of other heterologous protein variants in E. coli – the preferred host for expression and directed evolution studies

Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study

Background: Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective: The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods: We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results: The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29%. Conclusions: The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.https://doi.org/10.2196/1238

Machine Learning Approaches for Flow-Based Intrusion Detection Systems

In cybersecurity, machine/deep learning approaches can predict and detect threats before they result in major security incidents. The design and performance of an effective machine learning (ML) based Intrusion Detection System (IDS) depends upon the selected attributes and the classifier. This project considers multi-class classification for the Aegean Wi-Fi Intrusion Dataset (AWID) where classes represent 17 types of the IEEE 802.11 MAC Layer attacks. The proposed work extracts four attribute sets of 32, 10, 7 and 5 attributes, respectfully. The classifiers achieved high accuracy with minimum false positive rates, and the presented work outperforms previous related work in terms of number of classes, attributes and accuracy. The proposed work achieved maximum accuracy of 99.64% for Random Forest with supply test and 99.99% using the 10-fold cross validation approach for Random Forest and J48

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records (EHR) data for identifying such patients can ultimately help provide better health outcomes. Objective: Our study investigates the performance of predictive models to forecast HbA1c elevation levels by employing several machine learning models. We also investigate utilizing the patient's EHR longitudinal data in the performance of the predictive models. Explainable methods have been employed to interpret the decisions made by the blackbox models. Methods: This study employed Multiple Logistic Regression, Random Forest, Support Vector Machine and Logistic Regression models, as well as a deep learning model (Multi-layer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large dataset from Saudi Arabia with 18,844 unique patient records. Results: The machine learning models achieved promising results for predicting current HbA1c elevation risk. When employed with longitudinal data, the machine learning models outperformed the Multiple Logistic Regression model employed in the comparative study. The multi-layer perceptron model achieved an accuracy of 83.22% for the AUC-ROC when used with historical data. All models showed close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. Conclusions: This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Utilizing the patient's longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies

Assessing Innovations in High-Speed Rail Infrastructure

With a focus on infrastructure-related innovations for HSR, this paper aims at assessing their impacts in relation to the targets of punctuality, capacity, and life cycle costs. The paper presents a hybrid assessment methodology combing different approaches to assess effects on the named KPI. This contributes to reducing the existing gap that is found in the research literature

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.http://dx.doi.org/10.3390/electronics803032

Visual resolution under photopic and mesopic conditions in patients with Sjögren's syndrome

AIM: To focus on different visual resolution tasks under photopic and mesopic conditions in Sjögren's syndrome patients compared to age-matched healthy controls. METHODS: The visual resolution measurements included high and low visual acuities and contrast sensitivity functions. These tests were conducted under photopic and then mesopic conditions. Twenty-one Sjögren's syndrome patients and 21 aged-matched healthy volunteers completed all the measurements in this study. RESULTS: Sjögren's syndrome patients have greater impairment in contrast sensitivity than standardized visual acuity. This reduction was significant under the mesopic condition. Also, Sjögren's syndrome patients treated with pilocarpine suffer more than patients without pilocarpine treatment under low light conditions. CONCLUSION: Sjögren's syndrome patients shows greater impairment in different visual resolution tasks due to dry eye symptoms

The KPI-Model - an integrated KPi assessment methodology to estimate the impact of different innovations in the railway sector

The Shift2Rail Joint Undertaking (S2R) has set impact targets for the future rail system. Those targets of the KPIs, calculated by comparing future KPIs in the year 2030 to baseline KPIs as of 2013, are defined in the Shift2Rail Master Plan. These include among others to double the capacity (+100%), half the life cycle costs (LCC) (-50%) and to increase punctuality by improving reliability by 50%. In order to keep track of the realisation of these targets and to measure their degree of fulfilment a quantitative KPI model has been developed. The modelling approach and implementation are discussed in this contribution

Accelerated directed evolution of dye-decolorizing peroxidase using a bacterial extracellular protein secretion system (BENNY)

Background Dye-decolorizing peroxidases (DyPs) are haem-containing peroxidases that show great promises in industrial biocatalysis and lignocellulosic degradation. Through the use of Escherichia coli osmotically-inducible protein Y (OsmY) as a bacterial extracellular protein secretion system (BENNY), we successfully developed a streamlined directed evolution workflow to accelerate the protein engineering of DyP4 from Pleurotus ostreatus strain PC15. Result After 3 rounds of random mutagenesis with error-prone polymerase chain reaction (epPCR) and 1 round of saturation mutagenesis, we obtained 4D4 variant (I56V, K109R, N227S and N312S) that displays multiple desirable phenotypes, including higher protein yield and secretion, higher specific activity (2.7-fold improvement in kcat/Km) and higher H2O2 tolerance (sevenfold improvement based on IC50). Conclusion To our best knowledge, this is the first report of applying OsmY to simplify the directed evolution workflow and to direct the extracellular secretion of a haem protein such as DyP4