Parallel tensor factorization for relational learning

Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem. Tensor factorization is one of the ways to solve the link prediction problem. Many tensor factorization techniques have been devised in the last few decades, including Tucker, CANDECOMP/PARAFAC, and DEDICOM. RESCAL is one of the famous tensor factorization technique that can solve large scale problems with relatively less time and space complexity. The time complexity of RESCAL can further be reduced by making it parallel. This variant can also be applied to large scale datasets. This article focuses on devising a parallel version for RESCAL. A decent decrease in execution time has been observed in the execution of parallel RESCAL

Sentence Embedding Approach using LSTM Auto-encoder for Discussion Threads Summarization

Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome these limitations, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the performance of DTS. The empirical result in the context of the proposed approach’s average precision, recall, and F-measure with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets demonstrates the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks and boost the performance of the automated DTS model

Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

© 2014 IEEE. This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naïve Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate

COVID-19 Patient Count Prediction Using LSTM

IEEE In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients\u27 estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model\u27s prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients\u27 count of the proposed model is much closer to the actual patient count

Network pharmacology, molecular simulation, and binding free energy calculation-based investigation of Neosetophomone B revealed key targets for the treatment of cancer

In the current study, Neosetophomone B (NSP–B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues’ flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of −36.85 kcal/mol for the FGF6-NSP-B complex, −43.87 kcal/mol for the FGF20-NSP-B complex, and −37.42 kcal/mol for the FGF22-NSP-B complex, and −41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of −30.05 kcal/mol for the FGF6-NSP-B complex, −39.62 kcal/mol for the FGF20-NSP-B complex, −34.89 kcal/mol for the FGF22-NSP-B complex, and −37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy

Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea

AbstractMangroves are unique, and endangered, coastal ecosystems that play a vital role in the tropical and subtropical environments. A comprehensive description of the microbial communities in these ecosystems is currently lacking, and additional studies are required to have a complete understanding of the functioning and resilience of mangroves worldwide.In this work, we carried out a metagenomic study by comparing the microbial community of mangrove sediment with the rhizosphere microbiome of Avicennia marina, in northern Red Sea mangroves, along the coast of Saudi Arabia. Our results revealed that rhizosphere samples presented similar profiles at the taxonomic and functional levels and differentiated from the microbiome of bulk soil controls. Overall, samples showed predominance by Proteobacteria, Bacteroidetes and Firmicutes, with high abundance of sulfate reducers and methanogens, although specific groups were selectively enriched in the rhizosphere. Functional analysis showed significant enrichment in ‘metabolism of aromatic compounds’, ‘mobile genetic elements’, ‘potassium metabolism’ and ‘pathways that utilize osmolytes’ in the rhizosphere microbiomes.To our knowledge, this is the first metagenomic study on the microbiome of mangroves in the Red Sea, and the first application of unbiased 454-pyrosequencing to study the rhizosphere microbiome associated with A. marina. Our results provide the first insights into the range of functions and microbial diversity in the rhizosphere and soil sediments of gray mangrove (A. marina) in the Red Sea

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Monolayer Graphene Transfer onto Hydrophilic Substrates: A New Protocol Using Electrostatic Charging

In the present work, we developed a novel method for transferring monolayer graphene onto four different commercial hydrophilic micro/ultra-filtration substrates. The developed method used electrostatic charging to maintain the contact between the graphene and the target substrate intact during the etching step through the wet transfer process. Several measurement/analysis techniques were used in order to evaluate the properties of the surfaces and to assess the quality of the transferred graphene. The techniques included water contact angle (CA), atomic force microscopy (AFM), and field emission scanning electron microscopy (FESEM). Potassium chloride (KCl) ions were used for the transport study through the developed graphene-based membranes. The results revealed that 70% rejection of KCI ions was recorded for the graphene/polyvinylidene difluoride (PVDF1) membrane, followed by 67% rejection for the graphene/polyethersulfone (PES) membrane, and 65% rejection for graphene/PVDF3 membrane. It was revealed that the smoothest substrate was the most effective in rejecting the ions. Although defects such as tears and cracks within the graphene layer were still evolving in this new transfer method, however, the use of Nylon 6,6 interfacial polymerization allowed sealing the tears and cracks within the graphene monolayer. This enhanced the KCl ions rejection of up to 85% through the defect-sealed graphene/polymer composite membranes

Graphene Oxide-Based Membranes for Water Purification Applications: Effect of Plasma Treatment on the Adhesion and Stability of the Synthesized Membranes

The adhesion enhancement of graphene oxide (GO) and reduced graphene oxide (rGO) layer in the underlying polyethersulfone (PES) microfiltration membrane is a crucial step towards developing a high-performance membrane for water purification applications. In the present study, we modified the surface of a PES microfiltration membrane with plasma treatment (PT) carried out at different times (2, 10, and 20 min). We studied the effect of PT on the adhesion, stability, and performance of the synthesized GO/rGO-PES membranes. The membranes’ surface morphology and chemistry were characterized using atomic force microscopy, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy. The membrane performance was evaluated by conducting a diffusion test for potassium chloride (KCl) ions through the synthesized membranes. The results revealed that the 2 min PT enhanced the adhesion and stability of the deposited GO/rGO layer when compared to the other plasma-treated membranes. This was associated with an increase in the KCl ion rejection from ~27% to 57%. Surface morphology analysis at a high magnification was performed for the synthesized membranes before and after the diffusion test. Although the membrane’s rejection was improved, the analysis revealed that the GO layers suffered from micro/nano cracks, which negatively affected the membrane’s overall performance. The use of the rGO layer, however, helped in minimizing the GO cracks and enhanced the KCl ion rejection to approximately 94%. Upon increasing the number of rGO deposition cycles from three to five, the performance of the developed rGO-PES membrane was further improved, as confirmed by the increase in its ion rejection to ~99%