Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain

The State of the Art in Smart Grid Domain: A Network Modeling Approach

Agent-based computing and multi-agent systems are important tools in the domain of smart grid. Various properties of agents like self-organization, co-operation, autonomous behavior, and many others allow researchers to well represent the smart grid applications and models. From past few decades, various research attempts have been made in the smart grid domain by adopting the agent-based computing technology. The research publications are growing in number which makes it difficult to locate and identify the dynamics and trends in the research. Scientometric analysis is a useful tool to perform a comprehensive bibliographic review. It allows not only to understand the key areas of research but also provide visual representation of each entity involve in the research. In this study, we provide a detailed statistical as well as visual analysis of agent-based smart grid research by adopting complex network-based analytical approach. The study covers all scientific literature available online in Web of Science database. We are interested in identification of key papers, authors, and journals. Furthermore, we also investigate core countries, institutions, and categories.   </p

Genome-wide identification and characterization of bZIP transcription factors and their expression profile under abiotic stresses in Chinese pear (Pyrus bretschneideri)

Background: In plants, basic leucine zipper transcription factors (TFs) play important roles in multiple biological processes such as anthesis, fruit growth & development and stress responses. However, systematic investigation and characterization of bZIP-TFs remain unclear in Chinese white pear. Chinese white pear is a fruit crop that has important nutritional and medicinal values. Results: In this study, 62 bZIP genes were comprehensively identified from Chinese Pear, and 54 genes were distributed among 17 chromosomes. Frequent whole-genome duplication (WGD) and dispersed duplication (DSD) were the major driving forces underlying the bZIP gene family in Chinese white pear. bZIP-TFs are classified into 13 subfamilies according to the phylogenetic tree. Subsequently, purifying selection plays an important role in the evolution process of PbbZIPs. Synteny analysis of bZIP genes revealed that 196 orthologous gene pairs were identified between Pyrus bretschneideri, Fragaria vesca, Prunus mume, and Prunus persica. Moreover, cis-elements that respond to various stresses and hormones were found on the promoter regions of PbbZIP, which were induced by stimuli. Gene structure (intron/exon) and different compositions of motifs revealed that functional divergence among subfamilies. Expression pattern of PbbZIP genes differential expressed under hormonal treatment abscisic acid, salicylic acid, and methyl jasmonate in pear fruits by real-time qRT-PCR. Conclusions: Collectively, a systematic analysis of gene structure, motif composition, subcellular localization, synteny analysis, and calculation of synonymous (Ks) and non-synonymous (Ka) was performed in Chinese white pear. Sixty-two bZIP-TFs in Chinese pear were identified, and their expression profiles were comprehensively analyzed under ABA, SA, and MeJa hormones, which respond to multiple abiotic stresses and fruit growth and development. PbbZIP gene occurred through Whole-genome duplication and dispersed duplication events. These results provide a basic framework for further elucidating the biological function characterizations under multiple developmental stages and abiotic stress responses.This work was performed at the school of Life Sciences, Anhui agricultural university, Hefei, China and was supported by National Natural Science Foundation of China (No. 31640068) and Natural Science Youth Foundation of Anhui Agricultural University (No. 2019zd01). These funding bodies had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life and well-being. The occurrence of obesity is strongly associated with extreme health conditions, such as cardiac diseases, diabetes, hypertension, and some types of cancer. Therefore, it is vital to avoid obesity and or reverse its occurrence. Incorporating healthy food habits and an active lifestyle can help to prevent obesity. In this regard, artificial intelligence (AI) can play an important role in estimating health conditions and detecting obesity and its types. This study aims to see obesity levels in adults by implementing AI-enabled machine learning on a real-life dataset. This dataset is in the form of electronic health records (EHR) containing data on several aspects of daily living, such as dietary habits, physical conditions, and lifestyle variables for various participants with different health conditions (underweight, normal, overweight, and obesity type I, II and III), expressed in terms of a variety of features or parameters, such as physical condition, food intake, lifestyle and mode of transportation. Three classifiers, i.e., eXtreme gradient boosting classifier (XGB), support vector machine (SVM), and artificial neural network (ANN), are implemented to detect the status of several conditions, including obesity types. The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods, achieving overall performance rates of 98.5% and 99.6% in the scenarios explored

Efficient economic energy scheduling in smart cities using distributed energy resources

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2–A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2–B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids’ peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing carbon emissions, CPP-PDC reduces the maximum demand for electricity from the grid and the overall demand above the predetermined level. The maximum power demand from the grid is minimized for points A5 and B5, reducing up to 22% compared to A2. The proposed method minimizes both energy costs as well as CO2 emissions. A Pareto curve illustrates the trade-off between cost and CO2 emissions

Management of Primary Pterygium with Intralesional Bevacizumab (AVASTIN) Injection

Objective: To determine the management of primary Pterygium with intralesional Bevacizumab (AVASTIN) Injection. Study Design: Quasi-Experimental Study Place and Duration of Study: Armed Forces Institute of Ophthalmology, Rawalpindi Pakistan, from Oct 2019 to Mar 2020. Methodology: Sixty patients of Primary Pterygium with Grades 1, 2, and 3 were included. Pre-Intralesional injection evaluation includes the Ocular surface disease Index (OSDI), grading of Pterygium and ophthalmic examination, refraction,slit lamp bimicroscopy, fundoscopy, tonometry, and corneal topography. After four weeks of intralesional injection,reassessment was done. Results: A total of 60 participants with the mean age of the participants was 44.06±14.83 years were included in the study. In 26(43.3%) patients, grittiness, epiphora, redness, and photophobia were reported, and 16(26.6%) patients reported blurring of vision that improved in 100% of patients after intralesional injections. There was statistical significance (p-value ≤0.05) in means of K1, Sim K astigmatism, Surface asymmetry index, Surface Regularity Index, Grade of Pterygium, and Ocular surface disease index before and after the intralesional injection of Bevacizumab. However, no significant difference was recorded in Uncorrected Visual Acuity, Best Corrected Visual Acuity, and K2 parameters in pre and post-injection states (p-value ≥0.05).Only 7(11.6%) patients reported subconjunctival haemorrhage after the procedure. Conclusions: Treatment of Primary Pterygium with intralesional Bevacizumab injection successfully improves symptoms,Ocular Surface Disease Index, and reduces corneal astigmatism with minimum complications

Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain

Price-based demand response for household load management with interval uncertainty

In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. Robust optimization is a technique that is increasingly used in home energy management systems, where it is applied in the scheduling of household loads through demand side control. In this work, interruptible loads and thermostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris’ hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties

Effect of animal manure, crop type, climate zone, and soil attributes on greenhouse gas emissions from agricultural soils A global meta-analysis

Agricultural lands, because of their large area and exhaustive management practices, have a substantial impact on the earth's carbon and nitrogen cycles, and agricultural activities consequence in discharges of greenhouse gases (GHGs). Globally, greenhouse gases (GHGs) emissions especially carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from the agricultural sector are increasing due to anthropogenic activities. Although, the application of animal manure to the agricultural soil as an organic fertilizer not only improves soil health and agricultural production but also has a significant impact on GHGs emissions. But the extent of GHGs emissions in response to manure application under diverse environmental conditions is still uncertain. Here, a meta-analysis study was conducted using field data (48 peer-reviewed publications) published from 1989 to 2019. Meta-analysis results showed that poultry manure considerably increased CO2, CH4, and N2O emissions than pig and cattle manure. Furthermore, application of poultry manure also increased (¯(〖lnRR〗^ ) =0.141, 95% CI =0.526-0.356) GWP (global warming potential) of total soil GHGs emissions. While, the significant effects on CO2, CH4, and N2O emissions also occurred at manure rate > 320 kg N ha-1 and > 60% water filled pore space. The maximum concentrations of CO2, CH4, and N2O emissions were observed in neutral soils (¯(〖lnRR〗^ ) =3.375, 95% CI =3.323-3.428), alkaline soils (¯(〖lnRR〗^ ) =1.468, 95% CI =1.403-1.532), and acidic soils (¯(〖lnRR〗^ ) =2.355, 95% CI =2.390-2.400), respectively. Soil texture, climate zone and crop type were also found significant factors to increase GHGs emissions. Thus, this meta-analysis revealed a knowledge gap concerning the consequences of animal manure application and rate, climate zone, and physicochemical properties of soil on GHGs emissions from agricultural soils.Awais Shakoor would like to express his gratitude for the grant provided by the University of Lleida, Spain. The authors would like to appreciate the valuable comments from the editors and anonymous reviewers to improve the quality of this study

Opportunities for Physical Layer Security in UAV Communication Enhanced with Intelligent Reflective Surfaces

Unmanned Aerial Vehicles (UAVs) are an important component of next-generation wireless networks that can assist in high data rate communications and provide enhanced coverage.Their high mobility and aerial nature offer deployment flexibility and low-cost infrastructure support to existing cellular networks and provide many applications that rely on mobile wireless communications. However, security is a major challenge in UAV communications, and Physical Layer Security (PLS) is an important technique to improve the reliability and security of data shared with the assistance of UAVs. Recently, Intelligent Reflecting Surfaces (IRS) have emerged as a novel technology to extend and/or enhance wireless coverage by re-configuring the propagation environment of communications. This paper provides an overview of how IRS can improve the PLS of UAV networks. We discuss different use cases of PLS for IRS enhanced UAV communications and briefly review the recent advances in this area. Then based on the recent advances, we also present a case study that utilizes alternate optimization to maximize the secrecy capacity for IRS enhanced UAV scenario in the presence of multiple eavesdroppers. Finally, we highlight several open issues and research challenges to realize PLS in IRS enhanced UAV communications