A Secure Energy Aware Game Theory (SEGaT) Mechanism for Coordination in WSANs

A novel secure energy aware game theory (SEGaT) method has proposed to have better coordination in wireless sensor actor networks. An actor has a cluster of sensor nodes which is required to perform different action based on the need that emerge in the network individually or sometime with coordination from other actors. The method has different stages for the fulfilment of these actions. Based on energy aware actor selection (EAAS), selection of number of actors and their approach is the initial step followed by the selection of best team of sensors with each actor to carry out the action and lastly the selection of reliable node within that team to finally nail the action into place in the network for its smooth working and minimum compromise in the energy The simulations are done in MATLAB and result of the energy and the packet delivery ratio are compared with game theory (GaT) and real time energy constraint (RTEC) method. The proposed protocol performs better in terms of energy consumption, packet delivery ratio as compared to its competitive protocols.

Structures of LIG1 engaging with mutagenic mismatches inserted by polβ in base excision repair

DNA ligase I (LIG1) catalyzes final ligation step following DNA polymerase (pol) β gap filling and an incorrect nucleotide insertion by polβ creates a nick repair intermediate with mismatched end at the downstream steps of base excision repair (BER) pathway. Yet, how LIG1 discriminates against the mutagenic 3'-mismatches at atomic resolution remains undefined. Here, we determined X-ray structures of LIG1/nick DNA complexes with G:T and A:C mismatches and uncovered the ligase strategies that favor or deter ligation of base substitution errors. Our structures revealed that LIG1 active site can accommodate G:T mismatch in a similar conformation with A:T base pairing, while it stays in the LIG1-adenylate intermediate during initial step of ligation reaction in the presence of A:C mismatch at 3'-strand. Moreover, we showed mutagenic ligation and aberrant nick sealing of the nick DNA substrates with 3'-preinserted dG:T and dA:C mismatches, respectively. Finally, we demonstrated that AP-Endonuclease 1 (APE1), as a compensatory proofreading enzyme, interacts and coordinates with LIG1 during mismatch removal and DNA ligation. Our overall findings and ligase/nick DNA structures provide the features of accurate versus mutagenic outcomes at the final BER steps where a multi-protein complex including polβ, LIG1, and APE1 can maintain accurate repair.

Antimicrobial and Antioxidant Potential of Scenedesmus obliquus Microalgae in the Context of Integral Biorefinery Concept

Small-scale photobioreactors (PBRs) in the inoculum stage were designed with internal (red or green) and external white LED light as an initial step of a larger-scale installation aimed at fulfilling the integral biorefinery concept for maximum utilization of microalgal biomass in a multifunctional laboratory. The specific growth rate of Scenedesmus obliquus (Turpin) Kützing biomass for given cultural conditions was analyzed by using MAPLE software. For the determination of total polyphenols, flavonoids, chlorophyll “a” and “b”, carotenoids and lipids, UHPLC-HRMS, ISO-20776/1, ISO-10993-5 and CUPRAC tests were carried out. Under red light growing, a higher content of polyphenols was found, while the green light favoured the flavonoid accumulation in the biomass. Chlorophylls, carotenoids and lipids were in the same order of magnitude in both samples. The dichloromethane extracts obtained from the biomass of each PBR synergistically potentiated at low concentrations (0.01–0.05 mg/mL) the antibacterial activity of penicillin, fluoroquinolones or oregano essential oil against the selected food-borne pathogens (Staphylococcus aureus, Escherichia coli and Salmonella typhimurium) without showing any in vitro cytotoxicity. Both extracts exhibited good cupric ion-reducing antioxidant capacity at concentrations above 0.042–0.08 mg/mL. The UHPLC-HRMS analysis revealed that both extracts contained long chain fatty acids and carotenoids thus explaining their antibacterial and antioxidant potential. The applied engineering approach showed a great potential to modify microalgae metabolism for the synthesis of target compounds by S. obliquus with capacity for the development of health-promoting nutraceuticals for poultry farming.

Structures of LIG1 engaging with mutagenic mismatches inserted by polβ in base excision repair

DNA ligase I (LIG1) catalyzes final ligation step following DNA polymerase (pol) β gap filling and an incorrect nucleotide insertion by polβ creates a nick repair intermediate with mismatched end at the downstream steps of base excision repair (BER) pathway. Yet, how LIG1 discriminates against the mutagenic 3'-mismatches at atomic resolution remains undefined. Here, we determined X-ray structures of LIG1/nick DNA complexes with G:T and A:C mismatches and uncovered the ligase strategies that favor or deter ligation of base substitution errors. Our structures revealed that LIG1 active site can accommodate G:T mismatch in a similar conformation with A:T base pairing, while it stays in the LIG1-adenylate intermediate during initial step of ligation reaction in the presence of A:C mismatch at 3'-strand. Moreover, we showed mutagenic ligation and aberrant nick sealing of the nick DNA substrates with 3'-preinserted dG:T and dA:C mismatches, respectively. Finally, we demonstrated that AP-Endonuclease 1 (APE1), as a compensatory proofreading enzyme, interacts and coordinates with LIG1 during mismatch removal and DNA ligation. Our overall findings and ligase/nick DNA structures provide the features of accurate versus mutagenic outcomes at the final BER steps where a multi-protein complex including polβ, LIG1, and APE1 can maintain accurate repair.

Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples

Wastewater surveillance has become essential for monitoring the spread of SARS-CoV-2. The quantification of SARS-CoV-2 RNA in wastewater correlates with the Covid-19 caseload in a community. However, estimating the proportions of different SARS-CoV-2 strains has remained technically difficult. We present a method for estimating the relative proportions of SARS-CoV-2 strains from wastewater samples. The method uses an initial step to remove unlikely strains, imputation of missing nucleotides using the global SARS-CoV-2 phylogeny, and an Expectation-Maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different strains in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions accurately reflect the true proportions given sufficiently high sequencing depth and that the phylogenetic imputation is highly accurate and substantially improves the reference database.

State of the Art Review of Reinforcement Strategies and Technologies for 3D Printing of Concrete

This state of the art review paper aims to discuss the results of a literature survey on possible ways to reinforce printed concrete based on existing reinforcement strategies. Just as conventional concrete, for 3D printed concrete to be suitable for large-scale construction, reinforcement is needed to increase the tensile capacity of concrete members and reduce temperature and shrinkage cracking. Despite efforts that are currently underway, the development of proper reinforcement suitable for printed concrete is still very active on the research agenda. As an initial step for designing suitable reinforcement for printed concrete, the existing reinforcement methods for printed concrete as well as conventional cast concrete from the literature are reviewed and summarized. Through the preliminary evaluation of the suitability and effectiveness of various reinforcement methods, guidelines are proposed to better understand possible solutions to reinforce printed concrete and inspire new practical ideas to fill the current technology void. The conclusions also include the possible improvements of the existing reinforcement methods to be considered in future applications.

Ovine Endoscopic Ear Surgery - Accessible Smartphone-Based Training Model

Background: Understanding middle ear anatomy, in addition to endoscopic surgical skill acquisition, is an arduous task. Mastering 3-dimensional conceptualization and surgical dexterity may take many years. The coronavirus pandemic has made training difficult and complicated due to social distancing and risk of aerosolized viral spread in cadaver dissection. In this study we suggest a smartphone-based endoscope ovine head cadaveric dissection which is a simple, safe, and affordable training model for residents as an initial step in otologic endoscopic surgery training.Methods: A stepwise depiction of endoscopic ovine middle ear surgery; from cadaver and equipment acquisition, setting preparation, to surgical explanation and procedural steps. Results: The smartphone-based endoscopic otological ovine dissection model provides a low-cost, easily accessible and easily deployable training model for the novice surgeon world-wide. This model permits the novice surgeon a comprehensive anatomical understanding, middle ear proprioception, as well as a "safe" practicing model for diverse middle ear procedures. Conclusions: The ovine cadaver otological smartphone-based endoscopic surgery training model is an affordable, easy, reproducible, and transportable model, which makes it an ideal model from implementation in both low-middle and high-income countries.

Typecasting in the Recruitment of Full Professors

The recruitment of full professors is critical for the formation of academia. The professorship is critical not only for the prosperity of the HEIs, but especially so for the establishment, development and communication of the discipline. In this chapter, we analyze the initial step of the typecasting process in the recruitment of full professors. We use a few cases to illustrate how the intellectual and social organization of the field of education science(s) is manifested in publicly posted job advertisements. The analysis shows that the field is characterized by heterogeneity and no longer has a basis in one single discipline. New relations between research, teaching, and society can be observed, as well as a narrowing of authority of the professorship but an increase of responsibilities.

Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification

Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.

An Improved Text Extraction Approach with Auto Encoder for Creating Your Own Audiobook

As we all know, listening makes learning easier and interesting than reading. An audiobook is a software that converts text to speech. Though this sounds good, the audiobooks available in the market are not free and feasible for everyone. Added to this, we find that these audiobooks are only meant for fictional stories, novels or comics. A comprehensive review of the available literature shows that very little intensive work was done for image to speech conversion. In this paper, we employ various strategies for the entire process. As an initial step, deep learning techniques are constructed to denoise the images that are fed to the system. This is followed by text extraction with the help of OCR engines. Additional improvements are made to improve the quality of text extraction and post processing spell check mechanism are incorporated for this purpose. Our result analysis demonstrates that with denoising and spell checking, our model has achieved an accuracy of 98.11% when compared to 84.02% without any denoising or spell check mechanism.