Moderate Aortic Insufficiency with a Left Ventricular Assist Device Portends a Worse Long-Term Survival

The development of aortic insufficiency (AI) is known to be associated with prolonged left ventricular assist device (LVAD) support, but its overall significance with regards to long-term outcomes is unclear. This uncertainty translates to a lack of consensus regarding the management of AI in this patient population-an increasingly pertinent question as more patients are placed on LVAD support as destination therapy. A retrospective review of a single, high-volume institution was performed to assess outcomes in patients who received a HeartMate II or HeartWare (LVAD) between 2008 and 2018. Patients were stratified by AI severity at 6 months, and those with LVAD support of less than 6 months were excluded. The primary endpoint was 2 year mortality, and secondary endpoints were right heart failure and functional exercise capacity. At 6 month follow-up 111, 92, and 18 patients had no (0), mild (1), and moderate (2) AI, respectively. Moderate AI was a significant predictor of 2 year mortality in a multivariable model (p = 0.024). Functional exercise capacity (measured by 6 minute walk test) and incidence of right heart failure at 1 year were not significantly different between groups (P = 0.1421; P = 0.2189). In conclusion, moderate AI at 6 months post-LVAD implant is associated with worse long-term mortality. More aggressive management strategies targeting AI development in long-term LVAD patients may be warranted.

Bone Morphogenetic Protein-9–Stimulated Adipocyte-Derived Mesenchymal Progenitors Entrapped in a Thermoresponsive Nanocomposite Scaffold Facilitate Cranial Defect Repair

Due to availability and ease of harvest, adipose tissue is a favorable source of progenitor cells in regenerative medicine, but has yet to be optimized for osteogenic differentiation. The purpose of this study was to test cranial bone healing in a surgical defect model utilizing bone morphogenetic protein-9 (BMP-9) transduced immortalized murine adipocyte (iMAD) progenitor cells in a citrate-based, phase-changing, poly(polyethylene glycol citrate-co-N-isopropylacrylamide) (PPCN)-gelatin scaffold. Mesenchymal progenitor iMAD cells were transduced with adenovirus expressing either BMP-9 or green fluorescent protein control. Twelve mice underwent craniectomy to achieve a critical-sized cranial defect. The iMAD cells were mixed with the PPCN-gelatin scaffold and injected into the defects. MicroCT imaging was performed in 2-week intervals for 12 weeks to track defect healing. Histologic analysis was performed on skull sections harvested after the final imaging at 12 weeks to assess quality and maturity of newly formed bone. Both the BMP-9 group and control group had similar initial defect sizes (P = 0.21). At each time point, the BMP-9 group demonstrated smaller defect size, higher percentage defect healed, and larger percentage defect change over time. At the end of the 12-week period, the BMP-9 group demonstrated mean defect closure of 27.39%, while the control group showed only a 9.89% defect closure (P < 0.05). The BMP-9-transduced iMADs combined with a PPCN-gelatin scaffold promote in vivo osteogenesis and exhibited significantly greater osteogenesis compared to control. Adipose-derived iMADs are a promising source of mesenchymal stem cells for further studies in regenerative medicine, specifically bone engineering with the aim of potential craniofacial applications.

Optimization of the microwave-assisted extraction characteristics for bioactive compounds from eggplant (Solanum melongena L.)

Eggplant is consumed worldwide as a valuable source of phytochemicals, especially anthocyanin and antioxidants. Here, microwave-assisted extraction (MAE) was applied to optimize the total yield (TY), total anthocyanin content (TAC), total phenolic content (TPC), and radical scavenging activities (DPPH, ABTS, and FRAP) of eggplant. A response surface methodology (RSM) was employed based on a five-factor, three-level central composite design with the ethanol concentration (X1) 55-95% , microwave power (X2: 0-200 W), and extraction time (X3: 30-150 s) as independent process variables. Furthermore, the efficiency of MAE was compared to that of conventional reflux extraction (CRE) in terms of target responses, energy consumption, and CO2 emissions. The highest TY (1.72%), TAC (9.55 mg CE/L), TPC (48.75 mg GAE/100 mL), and antioxidant activities (DPPH: 45.95, ABTS: 46.74, and FRAP: 69.22 mg TE/100 mL) were obtained under the optimum MAE parameters of X1: 70%, X2: 160 W, and X3: 100 s. Moreover, MAE yielded higher target responses than CRE in a faster time with lower energy consumption and CO2 emissions. In conclusion, the application of RSM to evaluate the extraction characteristics of individual components by MAE and CRE revealed MAE as an effective method for the green extraction of target compounds from eggplant.

Transcriptome Profiling of Mouse Brain and Lung under Dip2A Regulation Using RNA-Sequencing

Regulation of nerve growth and patterning by cell surface protein disulphide isomerase

Modeling the Delivery of Coded Packets in D2D Mobile Caching Networks

Caching popular files on the mobile nodes have been seen as a promising solution to improve the network performance. In this paper, we analyze the delivery of coded packets of the requested file in a mobile caching network of nodes each with a few pre-cached packets of different files from a large file library. To model the packet delivery process, we develop a two-dimensional Markov chain framework where each request has a deadline requirement. For the delivery of packets, we consider the mobility aspect of caching network where the nodes request the files from other neighbors while moving among different cells and offload the requested files on the device-to-device link. To characterize the network performance, we derive closed-form expressions for file outage probability, the average delay in receiving file, and average throughput to provide some valuable insights on different parameter settings. The simulation results not only verify the accuracy of the analysis but also shed some light on the scenarios where the performance gap between the mobile and fixed caching networks is prominent.

Deep Learning Approach for Intelligent Intrusion Detection System

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware datasets available publicly are to be updated systematically and benchmarked. In this paper, a deep neural network (DNN), a type of deep learning model, is explored to develop a flexible and effective IDS to detect and classify unforeseen and unpredictable cyberattacks. The continuous change in network behavior and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm which can effectively work in detecting future cyberattacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs are chosen through the following hyperparameter selection methods with KDDCup 99 dataset. All the experiments of DNNs are run till 1,000 epochs with the learning rate varying in the range [0.01–0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets, such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS, and CICIDS 2017, to conduct the benchmark. Our DNN model learns the abstract and high-dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental testing, it is confirmed that DNNs perform well in comparison with the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.

Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data

Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based research such as data mining and machine learning has emphasized the issues of class distributions in datasets. In typical predictive modeling, the inability to effectively address a class imbalance in a real-life dataset is an important shortcoming of the existing machine learning algorithms. Most algorithms assume a balanced class in their design, resulting in poor performance in predicting the minority target class. Ironically, the minority target class is usually the focus in predicting processes. The misclassification of the minority target class has resulted in serious consequences in detecting chronic diseases and detecting fraud and intrusion where positive cases are erroneously predicted as not positive. This paper presents a new attribute selection technique called variance ranking for handling imbalance class problems in a dataset. The results obtained were compared to two well-known attribute selection techniques: the Pearson correlation and information gain technique. This paper uses a novel similarity measurement technique ranked order similarity-ROS to evaluate the variance ranking attribute selection compared to the Pearson correlations and information gain. Further validation was carried out using three binary classifications: logistic regression, support vector machine, and decision tree. The proposed variance ranking and ranked order similarity techniques showed better results than the benchmarks. The ROS technique provided an excellent means of grading and measuring the similarities where other similarity measurement techniques were inadequate or not applicable.

Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

Dealing with air pollution presents a major environmental challenge in smart city environments. Real-time monitoring of pollution data enables local authorities to analyze the current traffic situation of the city and make decisions accordingly. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Existing research has used different machine learning tools for pollution prediction; however, comparative analysis of these techniques is required to have a better understanding of their processing time for multiple datasets. In this paper, we have performed pollution prediction using four advanced regression techniques and present a comparative study to determine the best model for accurately predicting air quality with reference to data size and processing time. We have conducted experiments using Apache Spark and performed pollution estimation using multiple datasets. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of these regression models. Furthermore, the processing time of each technique through standalone learning and through fitting the hyperparameter tuning on Apache Spark has also been calculated to find the best-fit model in terms of processing time and lowest error rate.

Non-Conjugate Graphs Associated With Finite Groups

Let $G$ be a finite group and $S$ be a non-empty subset of $G$ comprising of the non-conjugate elements. In this study, we introduced the non-conjugate graph associated with $G$ with a coinciding set of vertices, such that two distinct vertices $x$ and $y$ are adjacent only if $x,y\in S$ . We then discussed some fundamental properties to ensure the algebraic and combinatorial structure of the graph. Afterward, we formulated the resolving set and resolving polynomial for a subclass of dicyclic groups.